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Abstract:

The invention provides control systems and methodologies for controlling a
process having computer-controlled equipment, which provide for optimized
process performance according to one or more performance criteria, such
as efficiency, component life expectancy, safety, emissions, noise,
vibration, operational cost, or the like. More particularly, the subject
invention provides for employing machine diagnostic and/or prognostic
information in connection with optimizing an overall business operation
over a time horizon.

Claims:

1. An apparatus operable in an industrial automation environment, the
apparatus comprising:a memory that retains instructions related
to:determining a current production rate of an industrial
process;predicting a theoretical capacity of the industrial process;
andcreating a visualization of the current production rate or the
theoretical capacity of the industrial process, the visualization
employed to drive the industrial process from the current production rate
to the theoretical capacity; anda processor, coupled to the memory,
configured to execute the instructions retained in the memory.

2. The apparatus of claim 1, the memory further retains instructions
related to:identifying a current bottleneck or historical bottleneck to
achieving the theoretical capacity of the industrial process; andbased at
least in part on the identifying, mitigating the current bottleneck or
the historical bottleneck to drive the industrial process from the
current production rate to the theoretical capacity, the identifying
includes at least one of determining a cost-benefit of removing the
current bottleneck or the historical bottleneck or is driven by a
financial return.

3. The apparatus of claim 1, the visualization permits tactile interaction
by a human intermediary to manipulate, in real-time, the visualization in
order to adjust at least one of the current production rate or the
theoretical capacity of the industrial process.

4. The apparatus of claim 3, the tactile interaction of the visualization
by the human intermediary includes varying at least one factor of
production to a target capacity associated with the at least one factor
of production.

5. The apparatus of claim 1, the memory further retains instructions
related to:performing dynamic constraint profiling based at least in part
on at least one of a current operating condition or a predicted operating
condition; andlinking to a corporate financial business system and
automatically quantifying a potential gain associated with increased
capacity affiliated with driving up the industrial process from the
current production rate to the theoretical capacity, the linking further
including associating with at least one of an external database or web
site to obtain one or more of current or predicted energy or commodity
costs.

6. The apparatus of claim 1, the memory further retains instructions
related to:utilizing a built-in framework for instantaneous analysis of
potential scenarios associated with an optimal capacity by one or more of
product, shift, or disparate production site; andbased at least in part
on the utilizing, analyzing tradeoffs associated with a plurality of
choices available to achieve the optimal capacity of the one or more of
product, shift, or disparate production site.

7. The apparatus of claim 6, the analyzing tradeoffs further comprising at
least one of proactively capturing a profitable opportunity or
proactively shedding a non-profitable opportunity.

8. The apparatus of claim 1, the memory further retains instructions
related to:ascertaining by production site the production site's top
constraints;quantifying a latent capacity available across the top
constraints; andbased at least in part on the quantifying, generating
financial profiles of production opportunities restricted by the top
constraints.

9. A method utilized in an industrial automation environment,
comprising:determining a current production rate of an industrial
process;predicting a theoretical capacity of the industrial process;
andcreating a visualization of the current production rate or the
theoretical capacity of the industrial process, the visualization
employed to drive the industrial process from the current production rate
to the theoretical capacity.

10. The method of claim 9, further comprising:identifying a current
bottleneck or historical bottleneck to achieving the theoretical capacity
of the industrial process; andbased at least in part on the identifying,
mitigating the current bottleneck or the historical bottleneck to drive
the industrial process from the current production rate to the
theoretical capacity.

11. The method of claim 9, the visualization permits tactile interaction
by a human intermediary to manipulate, in real-time, the visualization in
order to adjust at least one of the current production rate or the
theoretical capacity of the industrial process.

12. The method of claim 11, the tactile interaction of the visualization
by the human intermediary includes varying at least one factor of
production to a target capacity associated with the at least one factor
of production.

13. The method of claim 9, further comprising:performing dynamic
constraint profiling based at least in part on at least one of a current
operating condition or a predicted operating condition; andlinking to a
corporate financial business system and automatically quantifying a
potential gain associated with increased capacity affiliated with driving
up the industrial process from the current production rate to the
theoretical capacity.

14. The method of claim 9, further comprising:employing a built-in
framework for instantaneous analysis of potential scenarios associated
with an optimal capacity by one or more of product, shift, or disparate
production site; andbased at least in part on the employing, analyzing
tradeoffs associated with a plurality of choices available to achieve the
optimal capacity of the one or more of product, shift, or disparate
production site.

15. The method of claim 9, further comprising:ascertaining by production
site the production site's top constraints;quantifying a latent capacity
available across the top constraints; andbased at least in part on the
quantifying, generating financial profiles of production opportunities
restricted by the top constraints.

16. A system operable in an industrial automation environment,
comprising:means for determining a current production rate of an
industrial process;means for predicting a theoretical capacity of the
industrial process; andmeans for creating a visualization of the current
production rate or the theoretical capacity of the industrial process,
the visualization employed to drive the industrial process from the
current production rate to the theoretical capacity.

17. The system of claim 16, further comprising:means for identifying a
current bottleneck or historical bottleneck to achieving the theoretical
capacity of the industrial process; andbased at least in part on the
means for identifying, means for mitigating the current bottleneck or the
historical bottleneck to drive the industrial process from the current
production rate to the theoretical capacity.

18. The system of claim 16, further comprising:means for performing
dynamic constraint profiling based at least in part on at least one of a
current operating condition or a predicted operating condition; andmeans
for linking to a corporate financial business system and automatically
quantifying a potential gain associated with increased capacity
affiliated with driving up the industrial process from the current
production rate to the theoretical capacity.

19. The system of claim 16, further comprising:means for utilizing a
built-in framework for instantaneous analysis of potential scenarios
associated with an optimal capacity by one or more of product, shift, or
disparate production site; andbased at least in part on the means for
utilizing, means for analyzing tradeoffs associated with a plurality of
choices available to achieve the optimal capacity of the one or more of
product, shift, or disparate production site.

20. The system of claim 16, further comprising:means for ascertaining by
production site the production site's top constraints;means for
quantifying a latent capacity available across the top constraints;
andbased at least in part on the means for quantifying, means for
generating financial profiles of production opportunities restricted by
the top constraints.

21. The system of claim 16, the means for visualization further
comprising:means for generating one or more plausible scenario;means for
data mining information from one or more product process database;
andmeans for assigning at least one of a probabilistic number to a
predicted operating condition or a probabilistic number to a potential
gain, a most likely gain, or a least likely gain.

Description:

CROSS REFERENCE TO RELATED APPLICATIONS

[0001]This application is a continuation in part application of co-pending
U.S. Ser. No. 10/674,966, entitled SYSTEM AND METHOD FOR DYNAMIC
MULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION, INTEGRATION AND
UTILIZATION, filed on Sep. 30, 2003, which is a continuation in part
application of U.S. Ser. No. 10/214,927, entitled SYSTEM AND METHOD FOR
DYNAMIC MULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION, INTEGRATION
AND UTILIZATION, filed on Aug. 7, 2002, which claims the benefit of U.S.
Provisional Patent Application Ser. No. 60/311, 880, filed Aug. 13, 2001,
entitled INTELLIGENT PUMPING SYSTEMS ENABLE NEW OPPORTUNITIES FOR ASSET
MANAGEMENT AND ECONOMIC OPTIMIZATION, and U.S. Provisional Patent
Application Ser. No. 60/311, 596, filed Aug. 10, 2001, entitled
INTELLIGENT PUMPING SYSTEMS ENABLE NEW OPPORTUNITIES FOR ASSET MANAGEMENT
AND ECONOMIC OPTIMIZATION; the disclosures of which are hereby
incorporated by reference as if fully set forth herein.

TECHNICAL FIELD

[0002]The present invention relates to the art of dynamic diagnostics and
prognostics of systems, machines, processes and computing devices; and
more particularly the invention relates to control system and methods for
selecting, controlling and optimizing utilization of machinery primarily
in an industrial automation environment. The invention provides for
integration of control methods and strategies with decision support and
logistics systems to optimize specifically defined operational and
performance objectives.

BACKGROUND

[0003]The global economy has forced many businesses to operate and conduct
business in an ever increasingly efficient manner due to increased
competition. Accordingly, inefficiencies that were once tolerated by
corporations, due to a prior parochial nature of customers and suppliers,
now have to be removed or mitigated so that the respective corporations
can effectively compete in a vastly dynamic, global marketplace.
Furthermore, the intense desire to operate "green" facilities that are
environmentally friendly and to insure worker safety provides additional
motivation to minimize waste, scrap, and insure a reliable, safe process
that will not fail unexpectedly.

[0004]Many industrial processes and machines are controlled and/or powered
by electric motors. Such processes and machines include pumps providing
fluid transport for chemical and other processes, fans, conveyor systems,
compressors, gear boxes, motion control devices, HVAC systems, screw
pumps, and mixers, as well as hydraulic and pneumatic machines driven by
motors. Such motors are combined with other system components, such as
valves, pumps, furnaces, heaters, chillers, conveyor rollers, fans,
compressors, gearboxes, and the like, as well as with appropriate power
control devices such as motor starters and motor drives, to form
industrial machines and actuators. For example, an electric motor may be
combined with a motor drive providing variable electrical power to the
motor, as well as with a pump, whereby the motor rotates the pump shaft
to create a controllable pumping system.

[0005]The components parts used to build such motorized systems (e.g.,
pumps, motors, motor drives . . . ) are commonly chosen according to
specifications for a particular application or process in which the
motorized system is to be employed. For instance, a set of specifications
for a motorized pumping system may include fluid properties (e.g.
viscosity, specific gravity), suction head available, flow rates or
discharge pressures or ranges thereof, which the system must accommodate
for use in a particular application. In such a case, the pump is chosen
according to the maximum and minimum flow and head required in the
application, and the motor is selected based on the chosen pump hydraulic
power requirements, and other electrical and mechanical considerations.
The corresponding motor drive is selected according to the motor
specifications. Other pumping system components may then be selected
according to the chosen motor, pump, motor drive, control requirements,
and sensor input which may include motor speed sensors, pressure sensors,
flow sensors, and the like.

[0006]Such system design specifications are typically driven by maximum
operating conditions, such as the maximum flow rate the pumping system is
to achieve, which in turn drives the specifications for the component
parts. For instance, the motor may be selected according to the ability
to provide the necessary shaft speed and torque for the pump to achieve
the maximum required flow rate required for the process. Thus, the
typical motorized system comprises components rated according to maximum
operational performance needed. However, the system may seldom, if ever,
be operated at these levels. For example, a pump system rated to achieve
a maximum flow rate of 100 gallons per minute (GPM) may be operated at a
much lower flow rate for the majority of its operating life.

[0007]In facilities where such motorized systems are employed, other
operational performances characteristics may be of interest, apart from
the rated output of the motorized system. For instance, the cost of
operating a pumping system is commonly of interest in a manufacturing
facility employing the system. The component parts of such a pumping
system typically include performance ratings or curves relating to the
efficiency of the component parts at various operating conditions. The
energy efficiency, for example, may be a measure of the transferred power
of the component device, which may be expressed as a percentage of the
ratio of output power (e.g., power delivered by the device) to input
power (e.g., power consumed by the device). These performance curves
typically include one or more operating points at which the component
operates at maximum efficiency. In addition to the optimal efficiency
operating point, the components may have other operating points at which
other performance characteristics are optimal, such as expected lifetime,
mean time between failures (MTBF), acoustic emissions or vibration
output, time between expected servicing, safety, pollution emissions, or
the like.

[0008]While the operating specifications for the components in a motorized
(e.g., pumping) system may provide for component device selection to
achieve one or more system operational maxima (e.g., maximum flow rate
for a pumping system), other performance metrics (e.g., efficiency, cost,
lifetime, MTBF . . . ) for the components and/or the system of which they
form a part, are not typically optimal at the actual operating
conditions. Thus, even where the efficiency ratings for a pump, motor,
and motor drive in a motorized pumping system provide for maximum
efficiency at or near the maximum flow rate specified for the pumping
system, the efficiency of one or more of these components (e.g., as well
as that of the pumping system overall) may be relatively poor for other
flow rates at which the system may operate for the majority of the
service life thereof. In addition, motors, pumps, and drives are sized to
meet the application requirements. Each of these components have
different operating characteristics such that the efficient operating
point of a motor is at a different speed and load than the efficient
operating point of the connected pump. Separate selection of components
based on cost or individual efficiencies will result in an integrated
system that is sub-optimal with regard to efficiency, throughput, or
other optimization criteria.

[0009]Moreover, typically, the specification for such machines or
components thereof is performed at an isolated or level of granularity
such that higher-level aspects of a business or industrial concern are
overlooked. Thus, there is a need for methods and systems by which
efficiency and other performance characteristics associated with
selecting and utilizing motorized systems and components thereof may be
improved.

SUMMARY

[0010]The following presents a simplified summary of the invention in
order to provide a basic understanding of one or more aspects of the
invention. This summary is not an extensive overview of the invention. It
is intended to neither identify key or critical elements of the
invention, nor to delineate the scope of the present invention. Rather,
the sole purpose of this summary is to present some concepts of the
invention in a simplified form as a prelude to the more detailed
description that is presented hereinafter.

[0011]The subject invention provides for employing machine diagnostic
and/or prognostic information in connection with optimizing an overall
business operation. The scope of business operation can include
plant-wide or enterprise business objectives and mission objectives such
as for example that which may be required for aircraft, Naval ships,
nuclear, or military systems or components.

[0012]Systems, networks, processes, machines, computers . . . employing
the subject invention can be made to operate with improved efficiency,
less down-time, and/or extended life, and/or greater reliability, as well
enhancing systems/processes that are a superset thereof. Diagnostics
and/or prognostics in accordance with the invention can be effected
dynamically as well as in situ with respect to various
operations/processes. Moreover, the invention provides for optimizing
utilization of diagnostic/prognostic schemes via employment of a
utility-based approach that factors cost associated with taking an action
(including an incorrect action or no action) with benefits associated
with the action (or of inaction). Moreover, for example, such action can
relate to dissemination of the diagnostic/prognostic data and/or an
action taken in connection with an analysis of the data. The data
dissemination can be effected via polling techniques, beaconing
techniques, heartbeat schemes, broadcast schemes, watchdog schemes,
blackboard schemes, and/or a combination thereof. Accordingly, state
information can be employed in order to determine which scheme or
combination or order would lead to greatest utility in connection with
desired goal(s).

[0013]The subject invention provides for addressing concerns associated
with taking automated action in connection with valuable and/or critical
systems or methods. For example, security issues arise with respect to
permitting automated action--the subject invention provides for
employment of various security based schemes (e.g., authentication,
encryption, . . . ) to facilitate maintaining control as well as access
to such systems/processes. The invention can also take into consideration
levels of security and criticality of processes/systems of a network. For
example, automated action in connection with a critical process (e.g.,
power, life support, fire suppression, HVAC . . . can only be taken after
high security measures have been applied as well as such action only
being taken with a high-level confidence level (e.g., 99% probability of
a correct inference) that the automated action is the correct action to
take given the current evidence (e.g., current state information and
predicted state).

[0014]Moreover, another aspect of the invention provides for employment of
prognostics/diagnostics to optimize quality control of products to be
manufactured and/or delivered. For example, inference as to future state
of a component and effect of such future state on production of a product
can be employed as part of a closed-loop system that provides for
adjusting processing parameters in situ so as to dynamically correct for
variances associated with the inferred state that could impact quality
and/or quantity of the product. It is to be appreciated that such
techniques can be applied as part of an enterprise resource planning
(ERP) system to facilitate forecasting events/parameters (e.g., capacity,
supplier throughput, inventory, production, logistics, billing, design, .
. . ) that might impact an enterprise. As will be discussed in greater
detail infra, one particular aspect of the invention can employ
technologies such as radio frequency identification (RFID) tags in
connection with failure prediction, product throughput analysis, line
diagnosis, inventory management, and production control among other
technologies.

[0015]One particular aspect of the invention provides control systems and
methodologies for controlling a process having one or more motorized
pumps and associated motor drives, which provide for optimized process
performance according to one or more performance criteria, such as
efficiency, component life expectancy, safety, electromagnetic emissions,
noise, vibration, operational cost, or the like. For example, such
machine data can be employed in connection with inventory control,
production, marketing, utilities, profitablility, accounting, and other
business concerns. Thus, the present invention abstracts such machine
data so that it can be employed in connection with optimizing overall
business operations as compared to many conventional systems that employ
machine data solely in connection with machine maintenance, control and
possibly process control or optimal control methods.

[0016]Aspects of the subject invention employ various high-level data
analysis, modeling and utilization schemes in connection with providing
some of the advantages associated with the invention. For example,
Bayesian Belief Networks can be employed in connection with the subject
invention. A probabilistic determination model and analysis can be
performed at various levels of data to factor the probabilistic effect of
an event on various business concerns given various levels of uncertainty
as well as the costs associated with an making an incorrect inference as
to prognosing an event and its associated weight with respect to the
overall business concern. Statistical, probabilistic, and evidence or
belief-based, and/or various rule-based approaches can also be employed
in connection with the invention. The present invention takes into
consideration that the benefits of machinery monitoring and
condition-based maintenance can be significantly enhanced by integrating
real-time diagnostics and prognostics techniques within the framework of
an automatic control system. System operation can be prescribed based on
the predicted or probabalistic state or condition of the machinery in
conjunction with the anticipated workload or demand or probabalistic
demand and the business strategy along with other operational and
performance constraints. The generated decision space may be evaluated to
facilitate that suitably robust operational and/or machinery decisions
are made that maximize specified business objective(s) such as revenue
generation, life cycle cost, energy utilization, and/or machinery
longevity. Thus the subject invention integrates diagnostics and/or
prognostics with control linked with business objectives and strategies
to provide unique opportunities for dynamic compensating control and
ultimately for managing and optimizing system asset utilization. This may
be performed in consideration for uncertainty and belief in diagnostics
and prognostics, control and performance expectations, and business
uncertainties and likelihoods.

[0017]In accordance with another aspect of the invention, an intelligent
agent scheme can be employed wherein various machines, physical entities,
software entities, can be modeled and represented by intelligent software
agents that serve as proxies for the respective machines or entities.
These agents can be designed to interact with one another and facilitate
converging on various modifications and control of the machines of
entities in connection with efficiently optimizing an overall business
concern. Lower level agents can collaborate and negotiate to achieve
lower level process objectives in an optimal manner and integrate this
information to higher level agents. Agents, can compete with each other
for limited resources and become antagonistic in order to realize
critical objectives in a save, reliable, and optimum manner. Moreover,
the agents can comprise a highly distributed system controlling the
operation of a complex dynamic process. There may not exist a central
point or control or coordination of the system. Rather information is
distributed among the various agents. Groups of agents can form clusters
to promote meeting operational objectives such as local agent goals as
well as to promote collaboration in meeting higher-level system goals and
objectives. During negotiation for services and functions, local agents
can also provide "cost" information to other agents indicating
efficiency, energy utilization, or robustness for example. Agents can
assign functions and control modes to particular agents based on a
comparison and optimization of the specified cost function or operational
objective or objectives to be optimized.

[0018]Moreover, it is to be appreciated the subject invention can be
employed in connection with initial specification, layout and design of
an industrial automation system (e.g., process, factory) such that
high-level business objectives (e.g., expected revenue, overhead,
throughput, growth) are considered in connection with predicted machine
characteristics (e.g., life cycle cost, maintenance, downtime, health,
efficiency, operating costs) so as to converge on specifications, layout,
and design of the industrial automation system so that a mapping to the
high-level business objectives is more closely met as compared to
conventional schemes where such layout and design is performed in more or
less an ad hoc, manual and arbitrary manner. Integrating information
regarding opportunities for real-time prognostics and optimizing control
can influence the initial design and configuration of the system to
provide additional degrees of freedom and enhance the capability for
subsequent prognostics and optimizing and compensating control.

[0019]Predicted operating state(s) of the machine may be determined based
on expected demand or workload or a probabalistic estimate of future
workload or demand. Similarly, expected environment (e.g., temperature,
pressure, vibration, . . . ) information and possible expected damage
information may be considered in establishing the predicted future state
of the system. Undesirable future states of the system can be avoided or
deferred through a suitable change in the control while achieving
required operating objectives and optimizing established operational and
business objectives.

[0020]Discussing at least one aspect of the invention at a more granular
level, solely for sake of understanding one particular context of the
invention, control systems and methods are provided for controlling a
motorized system according to a setpoint (e.g., flow rate for a motorized
pump system), operating limits, and a diagnostic signal, wherein the
diagnostic signal is related to a diagnosed operating condition in the
system (e.g., efficiency, motor fault, system component degradation, pump
fault, power problem, pump cavitation . . . ). The invention thus
provides for controlled operation of motors and motorized systems,
wherein operation thereof takes into account desired process performance,
such as control according to a process setpoint, as well as one or more
other performance characteristics or metrics, related to the motorized
system and/or component devices therein, whereby improvements in
efficiency and other performance characteristics may be realized with
allowable process and machinery operating constraints via consideration
of prognostic and optimization data.

[0021]According to one aspect of the present invention, a method is
provided for controlling a motorized system. A desired operating point is
selected within an allowable range of operation about a system setpoint
according to performance characteristics associated with a plurality of
components in the system. For example, a flow rate setpoint may be
provided for a motorized pump system, and a range may be provided (e.g.,
+/-10%) for the system to operate around the setpoint flow value. This
range may correspond to a permissible range of operation where the
process equipment is making a good product. The system may be operated at
an operating point within this range at which one or more performance
characteristics are optimized in accordance with the invention. Thus, for
example, where an allowable flow control range and setpoint provide for
control between upper and lower acceptable flow rates, the invention
provides for selecting the operating point therebetween in order to
optimize one or more system and/or component performance characteristics,
such as life cycle cost, efficiency, life expectancy, safety, emissions,
operational cost, MTBF, noise, and vibration.

[0022]Where the motorized system includes an electric motor operatively
coupled with a pump and a motor drive providing electrical power to the
motor, the performance characteristics may include efficiencies or other
metrics related to the motor, the pump, and/or the motor drive. The
selection of the desired operating point may comprise correlating one or
more of motor efficiency information, pump efficiency information, and
motor drive efficiency information in order to derive correlated system
efficiency information. The desired operating point can then be selected
as the optimum efficiency point within the allowable range of operation
according to the correlated system efficiency information. The efficiency
of the individual component devices, and hence of the pumping system, may
be associated with the cost of electrical energy or power provided to the
system. Consequently, the invention can be employed to control the
pumping system so as to minimize power consumed by the system, within
tolerance(s) of the allowable range about the process setpoint.

[0023]The invention thus allows a system operator to minimize or otherwise
optimize the cost associated with pumping fluid, where for example, the
cost per unit fluid pumped is minimized. Alternatively or in combination,
other performance characteristics may be optimized or accounted for in
the optimization in order to select the desired operating point within
the allowable range. For instance, the component performance information
may comprise component life cycle cost information, component efficiency
information, component life expectancy information, safety information,
emissions information, operational cost information, component MTBF
information, MTTR, expected repair cost, noise information, and/or
vibration information. In this regard, it will be recognized that the
value of one or more system performance variables (e.g., temperature,
flow, pressure, power . . . ) may be used in determining or selecting the
desired operating point, which may be obtained through one or more
sensors associated with the system, a model of the system, or a
combination of these.

[0024]Another particular aspect of the invention provides a control system
for controlling a process having a pump with an associated motor. The
control system comprises a motor drive providing electrical power to the
motor in a controlled fashion according to a control signal, and a
controller providing the control signal to the motor drive according to a
desired operating point within an allowable range of operation about a
process setpoint. The controller selects the desired operating point
according to performance characteristics associated one or more
components in the process. The system can further comprise a user
interface for obtaining from a user, the setpoint, allowable operating
range, component performance information, and/or performance
characteristic(s), which are to be optimized.

[0025]In addition, the system can obtain such information from a host
computer and/or other information systems, scheduling systems, inventory
systems, order entry systems, decision support systems, maintenance
scheduling systems, accounting systems or control systems among others
within a larger process via a network or wireless communications.
Moreover, this information can be obtained via a wide area network or
global communications network, such as the Internet. In this regard, the
optimization of one or more performance characteristics can be optimized
on a global, enterprise-wide or process-wide basis, where, for example, a
single pump system may be operated at a less than optimal efficiency in
order to facilitate the operation of a larger (e.g., multi-pump) process
or system more efficiently. A specific pump may provide low throughput
and run inefficiently to meet minimum product requirements due to the
fact that another system in the enterprise can provide additional
processing at a much more cost-effective rate and will be run at maximum
throughput.

[0026]Yet another aspect of the invention provides for operating a
motorized system, wherein a controller operatively associated with the
system includes a diagnostic component to diagnose an operating condition
associated with the pump. The operating conditions detected by the
diagnostic component may include motor or pump faults, or failure and/or
degradation, and/or failure prediction (e.g., prognostics) in one or more
system components. The controller provides a control signal to the system
motor drive according to a setpoint and a diagnostic signal from the
diagnostic component according to the diagnosed operating condition in
the pump. The diagnostic component may perform signature analysis of
signals from one or more sensors associated with the pump or motorized
system, in order to diagnose the operating condition.

[0027]Thus, for example, signal processing may be performed in order to
ascertain wear, failure, remaining useful lifetime, or other deleterious
effects on system performance, whereby the control of the system may be
modified in order to prevent further degradation, extend the remaining
service life of one or more system components, or to prevent unnecessary
stress to other system components. In this regard, the diagnostic
component may process signals related to flow, pressure, current, noise,
vibration, and temperature associated with the motorized system. The
altered system control may extend the life of the machinery to maximize
throughput while insuring there is not failure for a specified period of
time and not longer. Having the machinery live longer than the minimum
necessary will require operating the machinery at an even lower level of
efficiency. For example our objective may be to maximize throughput or
efficiency while just meeting the minimum required lifetime and not
longer.

[0028]The aforementioned novel features of the subject invention can be
employed so as to optimize an overall business commensurate with set
business objectives. Moreover, as business needs/objectives change, the
invention can provide for dynamic adjustment and/or modification of
sub-systems (e.g., machines, business components, configurations, process
steps, . . . ) in order to converge toward the new operating mode that
achieves the business objective in an optimum manner. Thus, the subject
invention extracts and abstracts machine data (e.g., diagnostic and/or
prognostic data) and employs such data not only in connection with
optimizing machine utilization at a low level, but also to maximize
utilization of a machine given constraints associated with high-level
business objectives. Various models including simulation models,
rule-based system, expert system, or other modeling techniques may be
used to establish the range of possible operating conditions and evaluate
their potential for optimizing machinery operation.

[0029]It is to be appreciated that in addition to industrial applications,
the subject invention can be employed in connection with commercial (e.g.
HVAC) and military systems (e.g. Navy ships); and such employment is
intended to fall within the scope of the hereto appended claims.

[0030]To the accomplishment of the foregoing and related ends, the
invention, then, comprises the features hereinafter fully described. The
following description and the annexed drawings set forth in detail
certain illustrative aspects of the invention. However, these aspects are
indicative of but a few of the various ways in which the principles of
the invention may be employed. Other aspects, advantages and novel
features of the invention will become apparent from the following
detailed description of the invention when considered in conjunction with
the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0031]FIGS. 1a and 1b are schematic illustrations of prognostics systems
in accordance with various aspects of the subject invention.

[0032]FIG. 1c is a flow diagram illustrating state management in
accordance with a an aspect of the subject invention.

[0033]FIGS. 1d-1h illustrate system optimization aspects of the subject
invention.

[0034]FIG. 1i illustrates a scheme that facilitates achieving a
pre-planned, optimal future state in accordance with an aspect of the
subject invention.

[0035]FIG. 1j, illustrates another aspect of the subject invention
relating to establishing potential future state of a system/process.

[0036]FIG. 1k illustrates an enterprise resource planning system in
accordance with an aspect of the subject invention.

[0037]FIG. 2 illustrates exemplary operating levels of a pump system over
time in accordance with the subject invention.

[0040]FIG. 5 illustrates an exemplary belief network in accordance with
the subject invention.

[0041]FIG. 6 is a high level illustration of a distributed system in
accordance with the subject invention.

[0042]FIG. 7 illustrates a plurality of machines employing the subject
invention in connection with optimization.

[0043]FIG. 8 is a high-level flow diagram in accordance with one
particular aspect of the subject invention.

[0044]FIG. 9 is a side elevation view illustrating an exemplary motorized
pump system and a control system therefore with an optimization component
in accordance with an aspect of the present invention;

[0045]FIG. 10 is a schematic diagram illustrating further details of the
exemplary control system of FIG. 9;

[0046]FIG. 11 is a schematic diagram further illustrating the efficiency
optimization component and controller of FIGS. 9 and 10;

[0050]FIG. 15 is a plot showing an exemplary correlated pump system
efficiency optimization curve in accordance with the invention;

[0051]FIG. 16 is a schematic diagram illustrating an exemplary fluid
transfer system having multiple pump and valve controllers networked for
peer-to-peer communication according to an aspect of the invention;

[0052]FIG. 17 is a schematic diagram illustrating another exemplary fluid
transfer system having a host computer as well as multiple pump and valve
controllers networked for peer-to-peer and/or host-to-peer communication
according to an aspect of the invention;

[0053]FIG. 18 is a schematic diagram illustrating an exemplary
manufacturing system having multiple pump and valve controllers in which
one or more aspects of the invention may be implemented;

[0054]FIG. 19 is a flow diagram illustrating an exemplary method of
controlling a motorized pump in accordance with another aspect of the
invention; and

[0055]FIG. 20 is a side elevation view illustrating another exemplary
motorized pump system and a control system therefore with a diagnostic
component in accordance with another aspect of the invention.

[0056]FIG. 21 provides further illustration of an enterprise resource
planning component in accordance with an aspect of the claimed subject
matter.

[0057]FIG. 22 provides yet further illustration of an enterprise resource
planning component in accordance with various aspects of the claimed
subject matter.

[0058]FIG. 23 depicts a method that can be utilized to provide an energy
optimization model in accordance with an aspect of the claimed subject
matter.

[0059]FIG. 24 depicts a method that can be utilized to provide dynamic
capacity management in accordance with an aspect of the claimed subject
matter

[0060]FIGS. 25-31 illustrates various and disparate user manipulable
visual instrumentations that can be rendered by the claimed subject
matter

DETAILED DESCRIPTION

[0061]The present invention is now described with reference to the
drawings, wherein like reference numerals are used to refer to like
elements throughout. In the following description, for purposes of
explanation, numerous specific details are set forth in order to provide
a thorough understanding of the present invention. It may be evident,
however, that the present invention may be practiced without these
specific details. In other instances, well-known structures and devices
are shown in block diagram form in order to facilitate describing the
present invention.

[0062]As used in this application, the terms "component" and "system" are
intended to refer to a computer-related entity, either hardware, a
combination of hardware and software, software, or software in execution.
For example, a component may be, but is not limited to being, a process
running on a processor, a processor, an object, an executable, a thread
of execution, a program, and/or a computer. By way of illustration, both
an application running on a server and the server can be a component. One
or more components may reside within a process and/or thread of execution
and a component may be localized on one computer and/or distributed
between two or more computers.

[0063]As used herein, the term "inference" refers generally to the process
of reasoning about or inferring states of the system, environment, and/or
user from a set of observations as captured via events and/or data.
Inference can be employed to identify a specific context or action, a
system or component state or condition, or can generate a probability
distribution over states, for example. The inference can be
probabilistic--that is, the computation of a probability distribution
over states of interest based on a consideration of data and events and
the combination of individual probabilities or certainties. For example,
the probability of an observation can be combined with the probability
associated with the validity of the applicable inference rule or rules.
Inference can also refer to techniques employed for composing
higher-level events or conditions from a set of more basic level events,
conditions, observations, and/or data. Such inference results in the
construction of new events, conditions, or actions from a set of observed
events and/or stored event data, whether or not the events are correlated
in close temporal proximity, and whether the events and data come from
one or several event and data sources. Any of a variety of suitable
techniques for performing inference in connection with
diagnostics/prognostics in accordance with the subject invention can be
employed, and such techniques are intended to fall within the scope of
the hereto appended claims. For example, implicitly and/or explicitly
classifiers can be utilized in connection with performing a probabilistic
or statistical based analysis/diagnosis/prognosis--Bayesian networks,
fuzzy logic, data fusion engines, hidden Markov Models, decision trees,
model-based methods, belief systems (e.g., Dempster-Shafer), suitable
non-linear training schemes, neural networks, expert systems, etc. can be
utilized in accordance with the subject invention.

[0064]The subject invention provides for system(s) and method(s) relating
to employing machine data in connection with optimizing an overall system
or process. The machine data can be collected dynamically (e.g., in the
form of diagnostic data or control data) and/or generated in the form of
prognostic data relating to future machine state(s). The machine data can
be collected and/or generated in real-time (e.g., in situ, dynamically,
without significant lag time from origination to collection/generation).
The machine data can be analyzed and the analysis thereof employed in
connection with optimizing machine utilization as well as other business
components or systems (e.g., accounting, inventory, marketing, human
resources, scheduling, purchasing, maintenance manufacturing . . . ) so
as to facilitate optimizing an overall business objective or series of
objectives or concerns.

[0065]The invention provides methods and systems for controlling a
motorized system in order to achieve setpoint operation, as well as to
optimize one or more performance characteristics associated with the
system while operating within specified operating constraints. The
invention is hereinafter illustrated with respect to one or more
motorized pump systems and controls thereof. However, it will be
appreciated that one or more aspects of the invention may be employed in
operating other motorized systems, including but not limited to fans,
conveyor systems, HVAC systems, compressors, gear boxes, motion control
devices, screw pumps, mixers, as well as hydraulic and pneumatic machines
driven by motors. Further other non-motorized systems are included in the
scope of this invention including but not limited to ovens,
transportation systems, magnetic actuators, reaction vessels, pressurized
systems, chemical processes, and other continuous processes. For example,
the subject invention can be employed to facilitate prognosing wear of
metal and/or semiconductor contacts, switches, plugs, insulation,
windings, bushings, valves, seals, . . . so that they can be replaced or
repaired prior to failure. Thus, scheduling of thermographic inspections
for example can be conducted when actually required rather than on a
fixed schedule. The invention can also be applied to corrosion
prognostics as well as latency and/or node failure or backlog predictions
for network traffic. The invention can be applied over a time horizon
wherein time is factored into a utility-based diagnosis and/or prognosis
in connection with the subject invention. For example, value of
information, states, actions, inactions can vary as a function of time
and such value densities can be considered in connection with diagnostics
and/or prognostics in connection with the subject invention.

[0067]In addition, the attached figures and corresponding description
below illustrate the invention in association with optimizing system
and/or component efficiency, although it will be recognized that other
performance characteristics of a motorized system may be optimized
individually or in combination, which performance characteristics may
include, but are not limited to, life cycle cost, efficiency, life
expectancy, safety, throughput, emissions, operational cost, MTBF, noise,
vibration, energy usage, and the like. Furthermore, the aspects of the
invention may be employed to provide for optimization at a higher system
level, wherein a process comprises a plurality of motorized systems as
part of an overall automation system such that one or more performance
characteristics of the entire process are optimized globally. Moreover,
as discussed herein aspects of the invention can be employed in
connection with optimizing many higher level systems (e.g.,
business-based system).

[0068]The higher-level system optimization may prescribe not operating at
an optimum efficiency point with regard to energy utilization. Rather, a
more important, over-arching objective such as maximizing revenue
generation can supercede more narrow, limited scope objectives of
achieving lowest energy usage or extending machinery lifetime. The
subject invention employs a performance driven approach to leverage off
developments in diagnostic and prognostic algorithms, smart machines and
components, new sensor technologies, smart sensors, and integrate these
technologies among others in a framework of an enterprise-wide asset
management (EAM) system. The combination of optimizing methods and
processes in the framework of an EAM system comprise an Asset
Optimization System.

[0069]In addition to maintenance and repair costs, consideration for
issues such as operational impact, business strategy, and supply chain
(e.g., connected supplier-manufacturer-customer) issues are also
considered. There are several compelling business drivers that often make
cost-effective machinery reliability not only economically sound, but
also a business imperative. These recent business drivers include greater
concern for protecting the environment, ultimate concern for worker
safety, connected (e.g. virtual) organizations, make-to-order operating
strategy, pay-for-performance (e.g., power-by-the-hour), containing
warranty costs, and competitive time-based performance with greater
scrutiny and expectations in a rapidly expanding e-business world.

[0070]Although, the subject invention is primarily described in connection
with motors and pumps, it is emphasized that the subject invention
applies directly to other commercial and industrial process
machinery/systems. These systems could include for example a plant HVAC
system, a conveyor system, a semi-conductor fab line, chemical processing
(e.g. etching processes) or other continuous process or non-motor driven
machinery. Providing overall asset optimization as proposed herein can
require integrating and optimizing other non-motor components in a plant.
The scope of the subject invention as defined by the hereto appended
claims is intended to include all such embodiments and applications.

[0071]FIG. 1a illustrates a prognostics system 100 in accordance with one
particular aspect of the invention. A prognostics engine 110 is coupled
to a network 112--the coupling can be effected via hard-wire, wireless,
Internet, optics, etc. The prognostics engine receives data relating to
machines 114 or processes that are part of the network. The data is
dynamically analyzed within a desired context or set of rules for
example, and the engine 110 predicts/infers future state(s)/event(s)
relating to the devices, clusters thereof, tertiary devices (or clusters
thereof), processes, and/or the entire network. The prognostic engine 110
can employ extrinsic context data as represented via block 116--it is to
be appreciated that such context data (or a subset thereof) can be
provided by the machines as well as such context data being a priori
saved within the engine and/or a data store operatively coupled thereto.
The context data 116 for example can relate to future load, future
environment, possible mission scenario, expected stress, etc.

[0072]The prognostics can be done in the context of an expected future
environment, stress level, or mission. Several prognostic results can be
generated based on possible or probable future environment or stress
conditions. The prognostic data provided by the engine 112 can be
employed to take corrective action to mitigate undesirable effects
associated with the predicted state. The prognostic data can also be
employed to take automated action in order to optimize the network or a
subset thereof. Moreover, such data can be employed for forecasting,
trending, scheduling, etc. As shown, the machines 114 (or a subset
thereof) can also comprise diagnostic/prognostic components 118 that can
work with the prognostics engine 110 in connection with diagnosing and/or
prognosing the network and/or a subset thereof.

[0073]It is to be appreciated that the system can include a plurality of
prognostics engines 120 as shown in FIG. 1b. The engines can serve
different roles with respect to predicting various future states.
Moreover, the engines can be part of a hierarchical organization wherein
the hierarchy can include various levels of control and function such
that one engine may be an agent of another engine. Such arrangement can
provide for increasing speed of prognosis as well as isolating subsets of
the system for any of a variety of reasons (e.g., security, process
control, speed, efficiency, data throughput, load shedding . . . ).

[0074]As depicted in FIG. 1a, the invention can take the form of a
distributed prognostic system such that individual components can
respectively include prognostic engines that receive and analyze state
information with respect to the individual device(s). Accordingly, the
devices can communicate with one another and prognostic information
regarding device(s) can be shared as part of a collaborative effort to
improve accuracy of the aggregate system prognostics. It can also be
utilized to improve operations of an overall system. It is to be
appreciated that not all components of a network need to be intelligent
(e.g., comprise prognostics components), and that certain devices can
serve as an intelligent node with respect to other less intelligent
devices wherein the node and the other devices form a cluster. The
respective intelligent device can receive, monitor, and make predictions
as to future state of the cluster or subset thereof. It is to be
appreciated that the intelligent nodes need not be fixed to a particular
set of non-intelligent components, and that as part of a distributed
intelligent system, clusters can be dynamically generated based on
current state of a larger group of components and state of the system as
well as current and/or future needs/concerns.

[0075]Similarly, a group of intelligent system components can dynamically
re-configure based on the current system state or a predicted or possible
future system state. For example, a dynamic re-configuration may enable
the intelligent system components to more quickly or reliable detect and
respond to a system disturbance or fault that may possible occur in the
future. Accordingly, for example, in a critical event situation,
intelligent nodes can collaborate, negotiate use of resources, alter
function and control of intelligent components and share resources (e.g.,
processing resources, memory resources, transmission resources, cooling
capability, electrical power, . . . ) in order to collectively detect,
isolate, mitigate impact, circumvent and maintain critical services, and
restore functionality in an optimal manner. Of course, a utility-based
analysis can be employed in accordance with the invention wherein cost of
taking certain actions given evidence can be applied against benefit of
such action. Similarly, the cost-benefit of taking no action may be
analyzed. In addition, the probability of certain events, failures,
environments, and cost impact may be evaluated in the context of
uncertainty or probability. The resultant potential benefit from various
prescribed actions is established in a probabilistic content or as a
probability density value function. The resulting analysis and action
planning provides a basis for prescribing an operational plan and series
of decisions that will maximize system performance, business benefit, or
mission success with the highest probability.

[0076]In accordance with an alternative aspect of the invention,
intelligent components can broadcast state/event change information about
themselves or a cluster related thereto in a heartbeat type manner so
that information is disseminated upon change of state. Such beacon-type
scheme can facilitate optimizing network processing and transmission
bandwidth as compared to a polling scheme, for example. Moreover, as part
of an intelligent system, the broadcasting of data can be effected such
that devices that are or might be effected by such change of state are
notified while other devices do not receive such broadcast. The broadcast
can be daisy-chained wherein one change of state can effect state of
other devices, which change of state effects even other devices, and thus
the change of state info. can be part of a domino type data dissemination
scheme. It is to be appreciated that polling may also be desired in
certain situations and the invention contemplates polling in addition to
broadcast.

[0077]FIG. 1c illustrates a high level methodology 130 relating to
conveyance of state information. At 132 state data (e.g., change of state
information) is received. The state data can be received by a component
of the device wherein the change took place, or a node of a cluster may
receive data relating to change of state about and/or within the cluster
. . . . At 134 it is determined if such change of state is potentially
relevant to the device, cluster, network, tertiary devices, processes,
applications, individuals, entities, etc. If the data is relevant, at 136
the data is forwarded to where the state change data might be relevant.
If the data is not relevant, the process returns to 132 here change of
state is further monitored. At 138, the state change data is analyzed in
connection with making a diagnosis and/or prognosis. At 140, appropriate
action is taken in accordance with the analysis.

[0078]It is to be appreciated that other methodologies may be employed in
accordance with the subject invention. For example, at 132 the received
state change data can be in the form of a bit or flag being set, and such
information could be transmitted upon the change, or cached or queued
until appropriate to transmit. It is also to be appreciated that any
suitable data format (machine code, binary, hexadecimal, microcode,
machine language, flags, bits, XML, schema, fields, . . . ) and/or
transmission protocol/scheme/medium (http, TCP, Ethernet, DSL, optics,
RF, Internet, satellite, RF, . . . ) for carrying out the functionalities
described herein can be employed and such formats and protocols are
intended to fall within the scope of the hereto appended claims.

[0079]It is to be appreciated that a blackboard scheme may also be
employed in certain situations. In the blackboard scheme, an agent or
cluster will post a message or condition to the blackboard along with
appropriate source and context information. Other system components or
agents may query the blackboard to determine if any relevant information
is posted. It is also to be appreciated that an agent registry scheme may
also be employed in certain situations. A registry scheme requires
distributed agents to periodically register information such their
current operation, capabilities, capacities, and plans with a separate
resource facility. Operating as a "yellow pages" this registry is
available to other system agents who require additional facilities or
capabilities to meet current requirements. This registry is also
available to assist agents and agent clusters in negotiation and action
planning to address future possible scenarios. For example, the registry
may be used to establish a future configuration and operating scenario
from a set of possible contingency plans that will provide a less
disruptive or dangerous configuration in the event a recently detected
weakened component should fail. The weakened component may have indicated
its degraded state through a broadcast message as described above or by
updating the local cluster register.

[0080]Furthermore, a combination of broadcast, polling, blackboard, or
registry update schemes can be employed in connection with the invention
(e.g., as part of an optimization scheme) for conveyance of state change
information.

[0081]Component, device, subsystem, or process health or prognostic
information may be communicated in an explicit message using the
communication mechanism and architecture described above. Alternatively,
the machinery current condition and prognostic information may be
embedded in the communications message. The machinery health information
may be embedded in particular message segments reserved for machinery
health information. Diagnostic and prognostic status bits may be defined
and used by any intelligent machine on the network. The bits may be set
by the intelligent machine based on the machine's continuous health
self-assessment. Alternatively, adjacent intelligent components or
collaborating agents may report another agent or component is ineffective
in performing its function or perhaps is no longer able to function or no
longer reachable on the network.

[0082]Other schemes for encoding machinery diagnostic and prognostic
health information may be employed such as encoding this information in
the message header, or in the text of the message. Encryption schemes
that hide the encoded health information may be employed. This can
provide for lower message overhead and increase security and message
reliability. Alternatively, the characteristics of the message such as
message length, time of transmission, frequency of message transmission,
or scope of destination may convey device health and/or prognostic
information.

[0083]Instead of or in addition to providing state/event change
information about itself or the cluster it belongs to, related
information regarding future states or events may be provided. This
information provided may include an array with each element comprised of
three or more values. The values for each entry may be the future state
or event, the probability or likelihood of the event occurring and the
expected time or condition in the future that this event may occur with
the specified certainty.

[0084]It is to be appreciated that although the subject specification
primarily described the invention within the context of prognosis, the
invention is intended to encompass diagnostics as part of or in addition
to performing prognostics.

[0085]Various artificial intelligence schemes/techniques/systems (e.g.,
expert systems, neural networks, implicitly trained classifiers,
explicitly trained classifiers, belief networks, Bayesian networks, naive
Bayesian networks, HMMs, fuzzy logic, data fusion engines, support vector
machines, . . . ) can be employed in connection with making inferences
regarding future states in accordance with the subject invention. As such
an AI component in accordance with the subject invention can facilitate
taking a probability-based or statistics-based approach to performing
utility-based prognoses in accordance with the subject invention. It is
to be appreciated that the other embodiments of the invention can perform
automated action based on predicted state via simple rules-based
techniques (e.g., look-up table), for example, to mitigate processing
overhead. Moreover, a combination thereof can be employed as part of an
optimization scheme.

[0086]Turning to FIGS. 1d-h, the subject invention also contemplates a
closed-loop system that employs prognostics. A prognostics engine can be
used to predict future states or events relating to a system. The
predicted state or events can be, for example, quality of a product,
production throughput, possible line failure, machine temperature,
bearing failure, order arrival, feed stock quality, etc. The system can
employ such prognostic information to dynamically modify the system
and/or process (e.g., continuously cycling through the prognostics loop)
until convergence is achieved with respect to desired predicted future
state(s). Thus, prognostics in accordance with this aspect of the
invention can be employed as part of an in situ monitoring and
modification scheme to facilitate achieving a desired result. It is to be
appreciated that the state of the system will often dynamically change,
and the subject embodiment can be employed as part of a continuous
closed-loop system to not only converge on a desired state (including
predicted future state), but also to maintain such state, and mitigate
the system from entering into an unstable or undesired current or
predicted future state. Thus, the system can serve as a self-diagnosing
and correcting system.

[0087]A prediction or prognosis can indicate the expected future state of
the system or possible future states of the system with defined
probabilities based on the likelihood or probability of other outside
influencing factors. If the expected future state (or possible future
states) is acceptable, the system or plant may be monitored and
controlled to insure the expected state (or one of the possible expected
states) are realized. If the expected future state is unacceptable (e.g.,
tank rupture) then configuration or operating changes may be defined that
will put the system state trajectory on a more safe or desirable path.
Since a large suite of more desirable trajectories and future state
outcomes are possible, the most desirable, greatest benefit, most
valuable, and highest probability states may be selected. A closed loop
monitoring and control system will insure the system is progressing
toward the previously selected optimum or most desirable state.
Unexpected disturbances or new factors may cause the system to re-adjust
the state trajectory or alter the control as necessary. A goal can be to
define possible or likely future states, select critical states to avoid
and identify more desirable/optimum states. Then identify what may be
very slight control changes early to drive specific state variable(s) on
a prescribed (more desirable) trajectory subject to input constraints and
process constraints. A feedback mechanism including regular prognostics
and control alteration can insure that the system in on the correct, more
desirable trajectory resulting in achieving the pre-planned, optimal
state in the future as described in FIG. 1i.

[0088]RFIDs can also be employed in accordance with a particular aspect of
the invention. The RFIDs, can provide for component tracking and
monitoring such that the prognostics system, for example, as described
above can also participate in tracking and locating devices within a
system or process and optimize taking automated action in connection
therewith. For example, if a portion of a production line is predicted to
go down within a few seconds, components (produced in part) upstream from
the line about to go down can be quickly rerouted by the system as part
of an automated corrective action in accordance with the subject
invention. Accordingly, the RFID tags on the components can facilitate
quickly identifying current and predicted future location of thereof so
as to optimize the above action. It is to be appreciated that any
suitable scheme (e.g., global positioning system, RF-based, machine
vision, web-based . . . ) can employed with such aspect of the subject
invention. It is to be appreciated that many conventional GPS-type
system(s) are limited with respect to indoor tracking, and in such
situations, wireless based schemes can be employed to determine and/or
infer location of components.

[0089]A security component can be employed with prognostics in connection
with the subject invention. The inventors of the herein claimed invention
contemplate the potential dangers associated with taking automated action
based on inferred/predicted future state. Critical portions of a network,
system and/or process can be vulnerable to malicious and/or erroneous
action. Accordingly, security measures (e.g., data encryption, user
authentication, device authentication, trust levels, SOAP protocols,
public/private keys and protocols, virus control . . . ) can be employed
to mitigate undesired action and/or prognoses being performed in
connection with the subject invention. Accordingly, schemes for weighing
evidence, data integrity, security, confidence, pattern recognition, etc,
can be employed to facilitate that received data and prognoses with
respect thereto are accurate and reliable. Any suitable scheme for
effecting such measure can be employed in connection with the invention,
and are intended to fall within the scope of the hereto appended claims.
Moreover, another aspect of the invention can provide for an override
component that prevents a recommended automatic action being taken given
the cost of making an incorrect decision (e.g., turning off power,
initiating fire suppression, starting a ballast pump, turning off life
support . . . ).

[0090]Furthermore, if desired, certain aspects of a system or process can
be isolated (e.g., firewall) such that prognostics and automated action
in connection therewith cannot be taken on such isolated section. For
example, certain tasks may be deemed so critical that only a trusted and
authenticated human can take action in connection therewith. For example,
on a submarine, HVAC and power control may be deemed so critical that at
a certain part of control thereof, automated action is turned over to a
human. Likewise, such aspects of the subject invention can be employed to
mitigate undesired chain reactions (e.g., stock market crash of 1980s
wherein computers flooded the market with sell orders, East Coast
blackout of 2003 wherein a substantial portion of an integrated power
grid crashed as part of a load-shedding chain reaction . . . ). However,
it is to be appreciated that prognostics in accordance with the subject
invention can facilitate avoidance of entering into a chain reaction type
situation by making inference at a granular level and taking remedial
action to mitigate a low-level undesired state situation blossoming into
a larger, potentially catastrophic situation.

[0091]Accordingly, the invention contemplates performing a utility-based
approach in connection with a security-based approach to facilitate
taking optimal/appropriate actions given particular state(s) and context
thereof. Furthermore, some critical action such as turning off a pump,
may be deemed particularly sensitive and potentially dangerous. Before
this action is automatically invoked based on prognostics, it may be
required that two or more, independent system components (e.g. agent
clusters) may corroborate the expected or potential future state and
independently establish that the optimum course of action is to turn off
the pump or machinery. One of the several corroborating but independent
system components may be a human.

[0092]Another aspect of the subject invention analyzes not only state
information with respect to components, but also state information with
respect to extrinsic factors (e.g., ambient temperature, dust,
contaminants, pressure, humidity/moisture, vibration, noise, radiation,
static electricity, voltage, current, interference (e.g., RF), . . . )
that may effect future state of components. Accordingly, by predicting
future states as to such extrinsic factors and taking action in
connection with controlling such factors, various components can be
protected from entering into undesired future states. For example, many
failures of machines can be attributed to environmental influences (e.g.,
contamination) that can contribute to failure of the machine. By
monitoring and controlling such influences in a dynamic and proactive
manner, machine failure can be mitigated.

[0093]Referring to FIG. 1j, another aspect of the subject invention is to
establish the potential future state of the system given particular
operating scenarios, process runs, or mission scenarios. A suite of
possible operating conditions can be mapped against the present condition
of the system and system components to determine the likely outcome of
possible operating profiles or missions. If the outcome from some
possible operating scenarios is undesirable (e.g., catastrophic machinery
failure) then this future operating scenario may be avoided. For example,
a process run involving a high-temperature and high pressure reaction or
military mission over hostile territory of lengthy duration may indicate
likely gearbox or engine failure before successful completion. Performing
an analysis of the outcome of potential operating decisions or "what-if"
scenarios can provide a basis for optimizing the deployment of resources
and provide a superior measure of safety, security, and asset
optimization.

[0094]Yet another aspect of the subject invention provides for remote data
analysis and prognostics to be performed on a system. Accordingly, data
relating to a system/process can be collected and transmitted (e.g., via
the Internet, wireless, satellite, optical fiber . . . ) to a remote
prognostic engine that analyzes the data and makes inferences as to
future state of the system (or subset thereof) based in part on the data.
For example, a small facility in a rural location may operate numerous
motors and pumps in a harsh environment not necessarily suitable for
highly sensitive processing components. Accordingly, data can be gathered
at such location, and transmitted in real-time (or discrete time) and
analyzed at the remote location where the sensitive processing components
reside along with databases (e.g., historical data, trend data, machine
data, solutions data, diagnostic algorithms . . . ) that can facilitate
speedy analysis and diagnosis/prognosis of systems/machines/processes at
the rural location.

[0095]FIG. 1k is a high-level diagram illustrating one particular system
150 in connection with the subject invention. The system 150 includes a
plurality of machines 161 (MACHINE1 through MACHINEN--N being
an integer) at least a subset of which are operatively coupled in a
manner so as to share data between each other as well as with a host
computer 170 and a plurality of business components 180. The machines 161
include a respective diagnostic/prognostic component 182 that provides
for collecting and/or generating data relating to historical, current and
predicted operating state(s) of the machines. It is to be appreciated
that the plurality of machines can share information and cooperate; and
is it to be appreciated that the machines do not have to be the same.
Furthermore, some of the machines 161 may comprise sub-systems or
lower-level components that can have separate sensors, lifetime
estimates, etc. For example a compressor may consist of a motor, pump,
pressure chamber, and valves. The motor component may include smart
bearings with embedded sensors to predict bearing lifetime.

[0096]The predicted operating state(s) of the machine may be determined
based on expected demand or workload or a probabilistic estimate of
future workload or demand. Similarly, expected environment (e.g.,
temperature, pressure, vibration, information and possible expected
damage information may be considered in establishing the predicted future
state of the system. Undesirable future states of the system may be
avoided or deferred through a suitable change in the control while
achieving required operating objectives and optimizing established
operational and business objectives. Moreover, it is to be appreciated
that data relating to subsets of the machines can be aggregated so as to
provide for data relating to clusters of machines--the cluster data can
provide for additional insight into overall system performance and
optimization. The clusters may represent sub-systems or logical groupings
of machines or functions. This grouping may be optimized as a collection
of process entities. Clusters may be dynamically changed based on
changing operating requirements, machinery conditions, or business
objectives. The host computer 150 includes an enterprise resource
planning (ERP) component 184 that facilitates analyzing the machine data
as well as data relating to the business concern components 180
(utilities component 186, inventory component 188, processes component
190, accounting component 192, manufacturing component 194 . . . ). The
data is analyzed and the host computer 170 executes various optimization
programs to identify configurations of the various components so as to
converge more closely to a desired business objective. For example,
assume a current business objective is to operate in a just in time (JIT)
manner and reduce costs as well as satisfy customer demand. If the
inventory component 188 indicates that finished goods inventory levels
are above a desired level, the ERP component 184 might determine based on
data from the utility component 186 and machine components 160 that it is
more optimal given the current business objective to run the machines at
60% rather than 90% which would result in machinery prognostics
indicating we may extend the next scheduled maintenance down time for
another four months reducing the maintenance labor and repair parts
costs. This will also result in reducing excess inventory over a
prescribed period of time as well as result in an overall savings
associated with less power consumption as well as increasing life
expectancy of the machines as a result of operating the machines as a
reduced working rate.

[0097]It is to be appreciated that optimization criteria for machinery
operation can be incorporated into up-front equipment selection and
configuration activities--this can provide additional degrees of freedom
for operational control and enhanced opportunities for real-time
optimization.

[0098]Maintenance, repair, and overhaul (MRO) activities are generally
performed separate from control activities. Interaction and collaboration
between these functions are typically limited to the areas of operations
scheduling and to a lesser extent in equipment procurement--both are
concerned with maximizing production throughput of the process machinery.
Information from MRO systems and machinery control and production systems
are related and can provide useful information to enhance the production
throughput of process equipment. The subject invention leverages off
opportunities realized by closely coupling machinery health (e.g.
diagnostics) and anticipated health (e.g. prognostics) information with
real-time automatic control. In particular, the closed-loop performance
of a system under feedback control provides an indication of the
responsiveness, and indirectly, the health of the process equipment and
process operation. More importantly, it is possible to change how the
system is controlled, within certain limits, to alter the rate of
machinery degradation or stress. Using real-time diagnostic and
prognostic information the subject invention can be employed in
connection with altering future state(s) of the machinery. Given a
current operating state for both the machinery and the process the
subject invention can drive the machine(s) 160 to achieve a prescribed
operating state at a certain time in the future. This future operating
state can be specified to be an improved state than would occur if one
did not alter the control based on machinery health information.
Furthermore, the future state achieved could be optimal in some manner
such as machinery operating cost, machinery lifetime, or mean time before
failure for example. The prescribed operating state of a particular
machine may be sub-optimal however, as part of the overall system 150,
the system-wide operating state may be optimal with regard to energy
cost, revenue generation, or asset utilization.

The above data exhibits energy utilization from a motor-pump system under
conditions of full flow and reduced flow. The flow rate conditions shown
are achieved using a variable speed drive to control motor speed and
therefore flow rate (column 1) and with a motor running directly from the
power line with a throttling valve used to control flow rate (column 2).
The estimated energy savings with Drive Power at reduced flow is 0.468
kW--a 53% energy savings in connection with Drive Power. Pumping
applications which require operation at various prescribed head
Pressures, liquid levels, flow rates, or torque/speed values may be
effectively controlled with a variable speed motor drive. The benefits of
using a variable speed motor controller for pump applications are well
established, particularly for pumps that do not operate at full rated
flow all the time. In fact, the variable speed drive used for testing in
connection with the data of Table I has a user-selectable factory setting
optimized for fan and pump applications although these optimized settings
were not employed for the energy savings reported herein. The scope of
benefits beyond energy savings include improved machinery reliability,
reduced component wear, and the potential elimination of various
pipe-mounted components such as diverters and valves and inherent
machinery protection such as from over-current or under-current
operation. Pumps which typically operate at or near full synchronous
speed and at constant speed will not realize the energy savings as we
have demonstrated in Table I. Process conditions that require pump
operation at different flow rates or pressures (or are permitted to vary
operation within process constraints) are candidates to realize
substantial energy savings as we have shown. If maximum throughput is
only needed infrequently, it may be beneficial to specify the hydraulic
system and associated control to optimize performance over the complete
span of operating modes based on the time spent in each mode. It will be
necessary in this case to specify the duration of time the hydraulic
system is operating at various rating levels coupled with the throughput
and operating cost at each level.

[0100]Although machine control is discussed herein primarily with respect
to motor speed, the invention is not to be construed to have control
limited to such. Rather, there are other control changes that can be made
such as for example changing controller gains, changing carrier frequency
in the case of a VFD motor controller, setting current limits on
acceleration, etc. The control can be broad in scope and encompass many
simultaneous parameter changes beyond just speed. Moreover, the use of
models can be a significant component of control and configuration
optimization. A space of possible operating conditions for selection that
optimizes a given process or business performance may be determined by
employing a simulation model for example. Modeling techniques can also
serve as a basis for prognostics--thus, a simulation model can encompass
process machinery, throughput, energy costs, and business and other
economic conditions.

[0101]With respect to asset management, it is to be appreciated that the
system 100 may determine for example that purchasing several smaller
machines as compared to a single large machine may be more optimal given
a particular set of business objectives.

[0102]It is also to be appreciated that the various machines 161 or
business components 180 or a subset thereof can be located remotely from
one another. The various machines 161 and/or components 180 can
communicate via wireless or wired networks (e.g., Internet). Moreover,
the subject invention can be abstracted to include a plant or series of
plants with wireless or wired networked equipment that are linked via
long distance communications lines or satellites to remote diagnostic
centers and to remote e-commerce, distribution, and shipping locations
for dynamic logistics integrated with plant floor prognostics and
control. Thus, optimization and/or asset management in connection with
the subject invention can be conducted at an enterprise level wherein
various business entities as a whole can be sub-components of a larger
entity. The subject invention affords for implementation across numerous
levels of hierarchies (e.g., individual machine, cluster of machines,
process, overall business unit, overall division, parent company,
consortiums . . . ).

[0103]FIG. 2 illustrates operating levels over time of an exemplary pump
system. The few, rare excursions at maximum flow result in hydraulic
losses and energy losses during most of the operating time at lower flow
rates. Integrating the losses under a peak efficiency curve provides an
estimate of aggregate losses (and saving opportunity) for a target pump
applications. Aggregate pump level usage information is represented in a
very concise manner by Frenning, et al. (2001) in a duration diagram.
This diagram shows the number of hours per year needed at various flow
rates and provides a means to evaluate potential performance and energy
benefits through up-front system design and control specification. Beyond
these established benefits, there are important novel benefits associated
with integrating diagnostics and prognostics information with established
automatic motor control methods as discussed herein.

[0104]It is to be appreciated that the subject invention employs highly
sophisticated diagnostic and prognostic data gathering, generation and
analysis techniques, and should not be confused with trivial techniques
such as automatic disconnect based on an excessively high current or
temperature to be integrated diagnostics (e.g., something is wrong) and
control (e.g., automatic contact closure). For the purpose of
establishing an intelligent system for pump applications as described
above, we do not consider such machinery protection with bang-bang,
on-off control to be integrated diagnostics and control. Diagnostic
information as employed by the subject invention can be information
regarding a condition of system components or operating conditions that
will accelerate wear and hasten failure of critical system elements. For
example, information which identifies a level of degradation of a bearing
element, the degree of insulation capability lost, the amount of time
motor windings were operated at elevated temperature or that cavitation
is occurring is useful diagnostic information. Such information can be
combined to automatically alter prescribed control action, within
allowable limits, to maintain useful operation and potentially reduce
stress and degradation rate(s) of weakened components. The ultimate
effect is to defer, under controlled conditions, eventual machinery
failure.

[0105]Feedback control for pumping applications will often have one or
more process variables such as flow rate, head pressure, or liquid level
sensed by a transducer and converted to a digital signal. This digitized
signal is then input to a control computer where the sensed digitized
value is compared with the desired, setpoint value as discussed in
greater detail infra. Any discrepancy between the sampled value and the
setpoint value will result in a change in the control action to the
motor-pump system. The change to the motor-pump system may be a new
commanded valve position for a motor-operated valve or a new commanded
setpoint speed for a variable speed motor application.

[0106]Feedback control systems as described above are termed error-nulling
processes. We may represent the feedback controlled pumping system as a
lumped parameter linear system. The most general state space
representation of a linear, continuous time dynamical system can be
provided as:

{dot over (x)}=A(t)x(t)+B(t)u(t) (1)

y(t)=C(t)x(t)+D(t)u(t)

Here x(t) is the state vector representation of the system, u(t) is the
vector of real-valued inputs or control variables, and y(t) is the vector
of system real-valued outputs. Matrices A, B, C, and D represent the
plant or process state transitions, control input transition, state
output process, and direct input-output (e.g. disturbances) process
respectively. It is possible to incorporate diagnostic information into
this controller by altering the controller based on assessed equipment
health. For example, if the diagnostic analysis indicates that motor
windings are beginning to heat up we may alter the controller to reduce
the gain used to determine system input changes. This will result in a
system with less stress on the motor windings but at the expense of
slightly less system response. We may employ other techniques to shift
losses from weakened components to stronger system elements. If it is
determined through vibration analysis or current signature analysis
techniques that operation is at a critical or resonant frequency, we may
alter system speed to avoid such critical frequencies that may accelerate
wear of bearing components.

[0107]As another example, if we detect that cavitation is occurring in the
pump based on computed pump parameters and pump curves, we may reduce
motor speed to eliminate the degrading cavitation condition. In
particular, we may reduce speed to the point that adequate net positive
suction head available (NPSHA) is equal to the net positive suction head
required (NPSHR). As operating conditions changes and NPSHA increases,
then motor speed may be automatically increased to the point that maximum
flow is one again achieved while NPSHR<=NPSHA. A more detailed example
of an integrated diagnostic system with compensating control is described
below in the case study.

[0108]It is significant to note that in the absence of downstream
transducers for pressure and speed, the existence of many pumping
problems can be determined using only sampled motor current. For example,
with pumping systems, motor speed can be determined from motor current.
The existence of cavitation can be determined from a single phase of
motor current during pump operation. Such observation is significant
since pump curves are not required to perform this diagnosis and the
results are potentially more accurate since what is being sensed is a
specific feature indicative of cavitation rather than utilizing pressure,
flow, and pump nominal curves. Changes in viscosity, chemical
composition, and pump geometry such as from wear, will alter the accuracy
of the pump curves. MCSA techniques promise to be more accurate and less
invasive than more traditional pressure-flow measurements with pump
nominal design information.

[0109]Through various diagnostic means such as described above it is
possible to determine that an undesirable operating state is occurring or
that certain degraded components will result in early machinery failure.
Important benefits are possible by automatically altering the control to
avoid the higher-stress operating and control modes or to avoid stressing
weakened or degraded components and thereby extend the useful operating
life of machinery.

Prognostics & Control

[0110]Although process optimization has been employed for many years (e.g.
dynamic optimization) such as for continuous chemical processing
applications, unique and important benefits are possible by utilizing
machinery diagnostics and prognostic information to prescribe an optimum
control action dynamically. The benefits of integrated diagnostics and
control may be significantly expanded by utilizing information describing
the rate of degradation and remaining useful life of machinery under
various possible operating conditions. This permits changing the
operating mode to achieve a designated operating lifetime. Alternatively,
the control can be specified to minimize energy consumption and
maintenance costs or to maximize revenue generation. In extreme
conditions, the control may specified to achieve performance beyond the
normal operating envelope to protect the environment, avoid costly
losses, or protect worker safety while insuring that failure will not
occur during these extreme operating conditions. Prognostics with control
provides the foundation for overall process optimization with regard to
objectives such as efficiency, business strategies, maintenance costs, or
financial performance.

[0111]Implementing variable speed motor control for pumping applications
can provide direct savings in reduced energy consumption as described
herein. Additional benefits are possible by treating
drive-motor-pump-hydraulics as an integrated system. Combining individual
efficiency curves of a motor, pump, and drive permits generating a
composite system performance profile. This aggregate system model can be
used to diagnose the system as an integrated collection of coupled
elements and to prescribe a preferred operating state of the system.

[0112]In connection with the subject invention it is proposed to extend
the control model for the variable speed motor controller by
incorporating three additional elements in the control model.

[0113]The three elements that augment the control model are:
[0114]Specification of the allowable range of operation [0115]Diagnostic
& prognostic information, and [0116]Specification of optimal system
operation, processing objectives and business objectives

[0117]The first element in the control model is the capability to permit
operation within a range of process (state) variables. For example,
although a desired (e.g., setpoint) flow may be 100 gpm, however the
system may effectively run anywhere between 60 gpm and 110 gpm. The
specification of the allowable range of operation may include data
related to the sensitivity, accuracy, or marginal nature of the operating
bound. Probabilistic and time-dependency information may also be included
in the boundary specification.

[0118]The second element in the extended control model is information
relating to the health of the process machinery and its operation along
with information on the future health of the machinery such as rate of
degradation and remaining useful life. For example, one may determine
that the elevated temperature rise in the motor windings will reduce
insulation life by 1/2 or that the detected level of cavitation will
accelerate seal failure by 10 fold.

[0119]The third element in the extended control model is an analytic
representation of the operating objectives of the process or plant along
with any additional operating constraints. The representation of the
operating objectives of the process provides a quantifiable measure of
the "goodness of operation" and may include critical performance criteria
such as energy cost and process revenues. This permits establishing an
objective function that may subsequently be optimized through suitable
control changes. Additional operating constraints may include data such
as noise level, maximum process completion time. An objective function
specifying the process and business benefits may be optimized via dynamic
changes in the control action subject to not violating any of the process
operating constraints.

[0120]We can utilize established life expectancy models in conjunction
with classical control techniques to control the residual lifetime of
machinery. For example, crack growth models based on cyclic loading
provide a probabilistic model that can be embedded in a simulation model
to determine future stress due to vibration, temperature gradient, and
pressure. The Forman deterministic crack growth failure model provides a
basis for altering the stress and rate of crack growth directly from
changes in the control. The altered control then provides a quantitative
measure of the change in crack growth rate. This information can be used
to control the expected remaining lifetime of degraded components and
insure that failure does not occur before a tank is emptied or a
scheduled PM or machinery overhaul occurs.

[0121]The subject invention's focus of prognostics and distributed control
will enable future plant operations to be based on proactive operation
rather than reactive problem solving. Device alerts from remote
intelligent devices can warn of future potential problems giving time for
appropriate remedial or preventive action. Embedding operational
objectives and plant performance metrics into an automated
decision-making system can permit a high degree of machinery reliability
and avoid the unexpected process failures that impact quality and reduce
yields. Integrating prognostic information with automatic, real-time
decision making provides a basis for dynamic optimization and provides
unique, important benefits due to optimized plant operation.

Dynamic Optimization

[0122]Given that permissible operating modes have been suitably defined,
and established a means to project into the future possible or probable
operating states, and a criterion for judging preferred or optimal
performance the problem can be formulated as a classical optimal control
problem.

[0123]For example, if the operating objective is to minimize energy cost
per gallon pumped then the objective function will include flow
information, cost per kWh, and motor-drive power consumed. Dynamic
changes can be made to both the motor speed and drive internal parameters
to optimize the cost per gallon pumped subject to previously defined
process constraints. It is significant to note that the operating example
above will result in the least energy cost per gallon pumped; however, it
may also result in accelerated wear or thermal degradation of critical
machinery components. A more comprehensive operational model and
objective function may incorporate these additional parameters if
required. Additional parameters may include information such as expected
failure rate and failure cost for different operating modes, machinery
lifetime and capital replacement costs, and the impact on other connected
machines and processes such as valves, piping, and other process
machines.

[0124]One exemplary aspect of the subject invention establishes a control
method that will support decision making at each decision time interval
or control iteration loop. One principle of dynamic programming specifies
that if the system is at some intermediate point on an optimal path to a
goal then the remainder of the path must be on an optimal path from the
intermediate point to the goal. This permits making optimum choices of
the control variable, u(t), at time t that by only considering the need
to drive the system from state x(t) to X(tf), the final state of the
system. This approach provides an efficient technique for sequential
decision making while insuring that the complete system trajectory will
be optimum from time t0 to tf and we do not need to consider all
possible control options at every decision point simultaneously.

with defined initial conditions, time constraints, control variable and
state variable constraints. Here J represents an objective function value
to be minimized (or maximized). S and L are real-valued functions with S
representing cost penalty due to the stopping error at time tf (e.g.
wasted fluid not pumped or discarded useful life in replaced equipment).
L represents the cost or loss due to transient errors in the process and
the cost of the control effort during system operation.

[0127]For example, if the value of the stopping cost function is set at
S=0 and L=utu then:

MinJ=∫totfutu dt (3)

Equation 3 is a measure of the control effort or energy expended for a
process operating from time to time tf. This is termed the
least-effort problem and in the case of a drive-motor-pumping system,
results in completing a process segment (e.g. emptying a tank) at the
lowest possible energy cost.

[0128]When J is differentiable, gradient search techniques can be employed
to compute the desired change in control, u(t), that moves J closer to
the minimum (or maximum value). The concept of the gradient is
significant in that the change in the objective function we obtain from a
suitable control u(t) is proportional to the gradient, grad (J). This
provides a specification for the change in u needed to move J closer to
the optimum. If J is convex then local optimum values are not much of
concern and any optimum value obtained is a global optimum. This
formulation permits a step-by-step evaluation of the gradient of J and
the selection of a new control action to drive the system closer to an
optimum.

[0129]The gradient search technique, also called the method of steepest
decent is illustrated graphically in FIG. 3. Here each arrow represents a
new control decision in the quest to realize a minimum value for the
objective function, J. The specification of the optimal performance
metric, J, can incorporate information beyond energy utilization,
maintenance cost, or longevity of operation. For example, it is possible
to also formulate J to include strategic business information and asset
value information. In this manner selecting the sequence of optimal
control actions u(t) to optimize J will drive the system to achieve
optimum utilization of the assets involved.

Asset Optimization

[0130]The specification of the optimum operation of plant equipment
described above provides a flexible platform to incorporate various
business and operational factors. It is possible to include the cost of
maintenance for various failure modes, replacement and installation
costs, maintenance strategies, cost for scrap, re-work, line-restarting,
and revenue generation from the specified machinery. This permits the
generation and implementation of optimal asset lifetime management
policies across critical plant assets. The operational success of this
approach requires an effective Asset Register base, observability of key
state variables, and viable process and component models. The utilization
of open, industry standards for asset registry provides important
capabilities for integrating operating information across a manufacturing
plant and even across facilities. More recent developments have resulted
in an Open Systems Architecture for Condition-Based Maintenance that
provides a framework for real-time integration of machinery health and
prognostic information with decision support activities. This framework
spans the range from sensors input to decision support--it is open to the
public and may be implemented in a DCOM, CORBA, or HTTP/XML environment.

[0131]Often complex business and operational decisions are difficult to
incorporate into a single, closed-form objective function. In this case,
operating decision and control objectives may be decomposed into a suite
of sub-problems such that when taken together, the overall, more complex
problem is solved. For example, a process can be decomposed into a
pumping process, chemical reaction, and storage/batch transport problem.
These decompositions can be treated as individual sub-problems and
optimize each of these subject to boundary or interaction constraints
between each sub-problem. Alternatively, the decomposed problem can be
treated as a collection of coupled decision and seek an optimum that
balances possibly conflicting objectives and establishes a compromise
decision or control which is in some sense optimally global. For example,
an industry-wide drive to improve capital equipment utilization and
enhance RONA values may be in conflict with reducing maintenance costs
and maximizing revenue generation per energy unit consumed. Established
techniques for solving coupled and un-coupled optimization can be
employed to facilitate overall asset optimization. The compatibility of
control strategies with maintenance and scheduling strategies provides
new opportunities to optimize assets utilization. Automation control
actions may automatically be initiated, which reinforce and drive toward
strategic business objectives established by management. In accordance
with another particular example, an asset optimization system can
continually monitor energy costs via the Internet and dynamically change
machinery operation based on new energy costs to maximize revenue
generation. If energy costs become substantially high then the criteria
for energy-efficient operation can overtake the optimization criteria of
maximizing production throughput.

Real Options Analysis as a New Economic Tool Linking CBM Investments to
Business Strategy

[0132]In connection with machine and business state prognostics, asset
management and optimization in accordance with the subject invention, it
is to be appreciated that preventing unexpected equipment failures can
provide important operational and economic benefits. Using real options
pricing to provide a more accurate value of deferring machinery repair or
altering the control strategy. One aspect of the subject invention
provides for automatically checking the availability, cost, and
performance specifications of new components to replace healthy
component. Swapping out old, less efficient components with new, more
efficient components permits further optimizing process operation and
optimizing overall asset utilization.

[0133]The asset optimization program in connection with the subject
invention for example could launch a crawler or spider to search for
potential replacement components across the Internet. The asset
optimization system can for example continually monitor energy costs via
the Internet and dynamically change machinery operation based on new
energy costs to maximize revenue generation. If energy costs become high
enough then the criteria for energy-efficient operation will overtake the
optimization criteria of maximizing production throughput. Machinery
failure prevention can be enhanced by implementing a condition-based
maintenance (CBM) system with on-line, continuous monitoring of critical
machinery. An economic analysis required to justify CBM acquisitions
often follows a model used to evaluate other plant acquisitions. However,
traditional machinery acquisition valuation methods do not adequately
capture the operational and strategic benefits provided by CBM systems.

[0134]A financial model derived from options in financial markets (e.g.
puts and calls on shares or currencies) is proposed to facilitate
capturing unique and important benefits of CBM systems. In particular, a
CBM system inherently provides future decision and investment options
enabling plant personnel to avoid a future failure by making these
subsequent investments (exercising the option). Future options enabled by
an initial CBM investment provide economic benefits that are difficult to
capture with traditional capital asset pricing models. Real options
valuation methods are designed to capture the benefits of future
investment and strategic options such as those enabled by a CBM system.
Augmenting existing economic analysis methods with an option value
pricing model can capture, in financial terms, the unique and important
business benefits provided by CBM investments.

[0135]New developments in condition based monitoring algorithms, sensors,
communications, and architectures promise to provide new opportunities
for diagnostics and prognostics. CBM systems often require an incremental
investment beyond what is needed for basic manufacturing and automation
equipment. The acquisition of condition-based maintenance systems and
components must compete with other acquisition requests to obtain capital
from a limited pool of available funds. The costs associated with
implementing a CBM system are often easy to obtain although they may have
many components such as development, purchase, installation, support, and
calibration. However, it has traditionally been difficult to accurately
capture the benefits associated with a CBM investment. Augmenting
existing investment analysis methods with real option valuation methods
may provide a more accurate economic picture of the benefits from a CBM
investment opportunity. Investment decisions are typically based on a
traditional economic analysis of the funding opportunities available.
Traditional funding models such the capital asset pricing model (CAPM)
make assumptions regarding the investment required over time and the
expected financial return over time. These cash flows are brought back to
a net present value (NPV) level using an accepted discounting method and
rate. The discount rate is chosen to account for the cost of capital and
the inherent risk in the project. The investment analysis typically
provides a basis for a go/no-go decision on resource allocation. Once
approved, the funded project proceeds with cash flow proceeding as
prescribed in the project plan. In this respect, many plant acquisition
projects may be considered passive.

[0136]A significant and unique characteristic of a CBM investment is the
subsequent operational and investment options it provides management. A
CBM system does not inherently prevent a failure or automatically reduce
maintenance costs. A CBM system provides the essential information that
permits avoiding a failure or for minimizing maintenance or repair costs.
Realizing the benefits enabled by a CBM system requires active decision
making to initiate the indicated repair, operating changes, or
acquisitions. Similar to financial investment models such as put and
call, a CBM investment does not prevent a failure or automatically
generate profit, it affords an option to take action sometime in the
future (exercise an option) to realize a financial or operational
benefit. The option to make future decisions may be captured in an
economic model derived from financial investment futures. This technique,
called real options valuation, is directed at establishing an economic
value of an investment that includes the benefits (and costs) derived
from potential future investments. The potential future investment
options are enabled by the initial investment and they may be deferred,
exercised, or canceled at some time in the future when more information
in known. In this sense, real options valuation takes into account the
dynamic and active role of management over the life of the investment.

[0137]The subject invention can augment the traditional economic valuation
methods used for plant acquisitions with results from a real options
valuation to establish the value of a CBM investment. Condition based
maintenance systems provide information essential for establishing
effective reliability centered maintenance programs. Information
regarding the degree of machinery degradation, a diagnosis of an early
stage fault, and prognostics information such as remaining useful life
enable plant maintenance and operations personnel to take action to
minimize maintenance expenses and operations impact. A real options
approach to evaluating investments in machinery monitoring and diagnostic
systems may provide insight into the future value associate with
subsequent linked investment options. Investment in an initial CBM system
for example can provide future, more informed options to further expand
the core CBM system or to integrate the system into other business
information systems. Alternatively, information from the initial CBM
system can enable other operational investments that otherwise would not
be available. For example, a CBM system may provide a basis for
accelerating periodic maintenance, or may prescribe replacing equipment
just before failure and minimizing the amount of remaining useful life
that is discarded. Information from the CBM system may also provide
valuable information on when to exercise the upgrade or replacement
option.

[0138]The aforementioned examples and discussion are simply to convey the
numerous advantages associated with the subject invention. It is to be
appreciated that any suitable number of components and combination
thereof can be employed in connection with optimizing the overall system
100 in accordance with the present invention. Moreover, as a result of
the large number of combinations of components available in connection
with the subject invention some of the combinations will have known
correlations while there may exists other correlations not readily
apparent but yet still have an influence in connection with optimization
of the system 100. Accordingly, in connection with one particular aspect
of the invention data fusion can be employed in situations in order to
take advantage of information fission which may be inherent to a process
(e.g., vibration in the machine 110) relating to sensing a physical
environment through several different sensor modalities. In particular,
one or more available sensing elements may provide a unique window into
the physical environment where the phenomena to be observed is occurring
(e.g., in the motorized system and/or in a system of which the motorized
pumping system is a part). Because the complete details of the phenomena
being studied (e.g., detecting the operating state of the system or
components thereof) may not be contained within a single sensing element
window, there is information fragmentation which results from this
fission process. These information fragments associated with the various
sensing devices may include both independent and dependent components.

[0139]The independent components may be used to further fill out (or span)
the information space and the dependent components may be employed in
combination to improve the quality of common information recognizing that
all sensor data may be subject to error and/or noise. In this context,
data fusion techniques employed in the ERP system 132 may include
algorithmic processing of sensor data in order to compensate for the
inherent fragmentation of information because a particular phenomena may
not be observed directly using a single sensing element. Thus, data
fusion provides a suitable framework to facilitate condensing, combining,
evaluating and interpreting the available sensed information in the
context of the particular application. It will further be appreciated
that the data fusion may be employed in the diagnostics and prognostic
component 132 in order to employ available sensors to infer or derive
attribute information not directly measurable, or in the event of sensor
failure.

[0140]Thus, the present invention provides a data fusion framework and
algorithms to facilitate condensing, combining, evaluating and
interpreting various sensed data. The present invention also facilitates
establishing a health state of a system, as well as for predicting or
anticipating a future state of the machine(s) 110 and/or the system 100
(e.g., and/or of a sub-system of which the motorized pump system 110 is a
part). The data fusion system may be employed to derive system attribute
information relating to any number of attributes according to measured
attribute information (e.g., from the sensors) in accordance with the
present invention. In this regard, the available attribute information
may be employed by the data fusion system to derive attributes related to
failed sensors, and/or to other performance characteristics of the
machine(s) 110 and/or system 100 for which sensors are not available.
Such attribute information derived via the data fusion may be employed in
generating a diagnostics signal or data, and/or in performing control
functions in connection therewith.

[0141]In another example, a measured attributes may comprise flow and
pressure signals obtained from sensors associated with the machine 110
(e.g., pump), wherein the diagnostics system 132 provides a diagnostics
signal indicative of pump cavitation according to measured flow and
pressure signals. The invention thus provides for health indications
relating to component conditions (e.g., wear, degradation, faults,
failures, etc.), as well as those relating to process or systems
conditions, such as cavitation in the pump 110. The diagnostics system
132 may comprise a classifier system, such as a neural network, detecting
pump cavitation according to the measured flow and pressure signals,
which may be provided as inputs to the neural network. The cavitation
indication in the resulting diagnostics signal or data may further be
employed to modify operation of the machine 110 and/or system 100, for
example, in order to reduce and/or avoid such cavitation. Thus, an
appropriate control signal may be provided by a controller to a motor
drive in connection with the pump 110 in order to avoid anticipated
cavitation, based on the diagnostics signal (e.g., and/or a setpoint),
whereby the service lifetime of one or more system components (e.g.,
pump) may be extended.

[0142]In another related example, cavitation (e.g., actual or suspected)
in the pump 110 may be detected via measured (e.g., or derived) current
signal measurements, for example, via a sensor. The diagnostics system
132 in this instance may provide a diagnostics signal indicative of pump
cavitation according to the measured current. In order to detect
cavitation using such current information, the diagnostics system 132 may
employ the neural network to synthesize a change in condition signal from
the measured current. In addition, the diagnostics system 132 may further
comprise a preprocessing portion (not shown) operatively coupled to the
neural network, which conditions the measured current prior to inputting
the current into the neural network, as well as a post processing portion
operatively coupled to the neural network to determine whether the change
in condition signal is due to a fault condition related to a motorized
system driving the pump 110. In this regard, the post processing portion
may comprise a fuzzy rule based expert system. In addition, the
diagnostics system 132 may detect one or more faults relating to the
operation of the pump 110 and/or one or more faults relating to the
operation of a motor driving the pump 110 according to the measured
current.

[0143]Other faults may be detected and diagnosed using the diagnostics and
control system 132 of the invention. For instance, the diagnostics system
132 may be adapted to obtain a space vector angular fluctuation from a
current signal (e.g., from a current sensor) relating to operation of the
motor driving the pump, and further to analyze the space vector angular
fluctuation in order to detect at least one fault in the motorized
system. Such faults may include, for example, stator faults, rotor
faults, and/or an imbalance condition in the power applied to the motor
in the motorized system.

[0144]In this situation, the diagnostics/prognostic system 132 may obtain
a current signal associated with the motor from the sensor, and calculate
a space vector from the current signal. The diagnostics/prognostic system
132 determines a space vector angular fluctuation from the space vector,
and analyzes the space vector angular fluctuation in order to detect one
or more faults associated with the motor driving the pump 110. For
instance, first, second, and third phase current signals associated with
the motorized system may be sampled in order to obtain the current
signal, and corresponding first, second, and third phase space vectors
may be computed in the diagnostics/prognostic system 132.

[0145]A resulting space vector may then be calculated, for example, by
summing the first, second, and third phase space vectors. The
diagnostics/prognostic system 132 may then compare the space vector with
a reference space vector, wherein the reference space vector is a
function of a constant frequency and amplitude, and compute angular
fluctuations in the space vector according to the comparison, in order to
determine the space vector angular fluctuation. The
diagnostics/prognostic system 132 then performs frequency spectrum
analysis (e.g., using an FFT component) of the space vector angular
fluctuation to detect faults associated with the motorized system. For
example, motor faults such as rotor faults, stator faults, and/or
unbalanced supply power associated with the pump motor may be ascertained
by analyzing the amplitude of a first spectral component of the frequency
spectrum at a first frequency, wherein the diagnostics/prognostic system
132 may detect fluctuations in amplitude of the first spectral component
in order to detect one or more faults or other adverse conditions
associated with the motorized system. In this regard, certain frequencies
may comprise fault related information, such as where the first frequency
is approximately twice the frequency of power applied to the motor
driving the pump. Alternative to generating a full spectrum, the
diagnostics/prognostic system 132 may advantageously employ a Goertzel
algorithm to extract the amplitude of the first spectral component in
order to analyze the amplitude of the first spectral component. The
diagnostics/prognostic signal indicating such motor faults may then be
employed by a controller to modify operation of the pumping system 110 to
reduce or mitigate such faults. The above discussion in connection with
FIG. 1 was presented at a high-level--FIGS. 9 and 20 should be referenced
in connection with details regarding the motor, drivers, sensors,
controllers, etc.

[0146]FIG. 4 illustrates an aspect of the subject invention wherein at
least a subset of the machines or components are represented via
intelligent software agents. For example, each of the respective machines
110 (FIG. 1a) can be represented by respective intelligent agents
(MACHINE AGENT1 through MACHINE AGENTN--N being an integer),
and various business concerns represented by respective agents (e.g.,
BUSINESS AGENT1 through BUSINESS AGENTM--M being an integer).
The intelligent agents can be software models representative of their
various physical or software counterparts, and these agents can serve as
proxies for their counterparts and facilitate execution of various
aspects (e.g., machine or component interaction, modification,
optimization) of the subject invention. The agents can be designed (e.g.,
appropriate hooks, interfaces, common platform, schema, translators,
converters . . . ) so as to facilitate easy interaction with other
agents. Accordingly, rather than executing an optimization algorithm for
example on a respective device directly, such algorithms can be first
executed on the respective agents and than once the system 100 decides on
an appropriate set of modifications the final modifications are
implemented at the agent counterparts with the agents carrying the
instructions for such modifications.

[0147]The proliferation of distributed computing systems and enhanced
prognostic, control, and optimization techniques provides via the subject
invention for changing the landscape of industrial automation systems.
The aforementioned framework complements technical capabilities for asset
optimization via an agent based representation. Agents may be considered
autonomous, intelligent devices with local objectives and local decision
making. These agents however can be part of a larger collection of agents
and possess social and collaborative decision making as well. These
capabilities permit localized, distributed agents to collaborate and meet
new, possibly unforseen operational conditions. In addition, through
collaboration, some agents may choose to operate in a sub-optimal mode in
order to achieve some higher level objective such as asset optimization,
process safety, or overall process energy optimization.

[0148]FIG. 5 illustrates a representative belief network 500 that can be
are used to model uncertainty in a domain in connection with the subject
invention. The term "belief networks" as employed herein is intended to
encompass a whole range of different but related techniques which deal
with reasoning under uncertainty. Both quantitative (mainly using
Bayesian probabilistic methods) and qualitative techniques are used.
Influence diagrams are an extension to belief networks; they are used
when working with decision making. Belief networks are employed to
develop knowledge based applications in domains which are characterized
by inherent uncertainty. A problem domain is modeled as a set of nodes
510 interconnected with arcs 520 to form a directed acyclic graph as
shown in FIG. 5. Each node represents a random variable, or uncertain
quantity, which can take two or more possible values. The arcs 520
signify the existence of direct influences between the linked variables,
and the strength of each influence is quantified by a forward conditional
probability.

[0149]Within the belief network the belief of each node (the node's
conditional probability) is calculated based on observed evidence.
Various methods have been developed for evaluating node beliefs and for
performing probabilistic inference. The various schemes are essentially
the same--they provide a mechanism to propagate uncertainty in the belief
network, and a formalism to combine evidence to determine the belief in a
node. Influence diagrams, which are an extension of belief networks,
provide facilities for structuring the goals of the diagnosis and for
ascertaining the value (the influence) that given information will have
when determining a diagnosis. In influence diagrams, there are three
types of node: chance nodes, which correspond to the nodes in Bayesian
belief networks; utility nodes, which represent the utilities of
decisions; and decision nodes, which represent decisions which can be
taken to influence the state of the world. Influence diagrams are useful
in real world applications where there is often a cost, both in terms of
time and money, in obtaining information.

[0150]An expectation maximization (EM) algorithm is a common approach for
learning in belief networks. In its standard form it does not calculate
the full posterior probability distribution of the parameters, but rather
focuses in on maximum a posteriori parameter values. The EM algorithm
works by taking an iterative approach to inference learning. In the first
step, called the E step, the EM algorithm performs inference in the
belief network for each of the datum in the dataset. This allows the
information from the data to be used, and various necessary statistics S
to be calculated from the resulting posterior probabilities. Then in the
M step, parameters are chosen to maximize the log posterior logP(T|D,S)
given these statistics are fixed. The result is a new set of parameters,
with the statistics S which we collected are no longer accurate. Hence
the E step must be repeated, then the M step and so on. At each stage the
EM algorithm guarantees that the posterior probability must increase.
Hence, it eventually converges to a local maxima of the log posterior.

[0151]FIG. 6 illustrates an aspect of the invention in which the invention
is employed as part of a distributed system 600 rather than via a host
computer (FIG. 1a). Thus, the various components in the system 600 share
processing resources and work in unison and/or in subsets to optimize the
overall system 600 in accordance with various business objectives. It is
to be appreciated that such distributed system can employ intelligent
agents (FIG. 2) as described supra as well as belief networks (FIG. 5)
and the ERP components 132 (FIG. 1a) and data fusion described above in
connection with the system 100. Rather than some of these components
(ERP, data fusion) being resident on a single dedicated machine or group
of machines, they can be distributed among any suitable components within
the system 600. Moreover, depending on which threads on being executed by
particular processors and the priority thereof, the components may be
executed by a most appropriate processor or set of processors given the
state of all respective processors within the system 600.

[0152]FIG. 7 illustrates another aspect of the subject invention wherein
the invention is implemented among the respective machines 710 in
connection with optimizing use thereof. For example, the
diagnostic/prognostic components 732 can exchange and share data so as to
schedule maintenance of a particular machine, or load balance.

[0153]Returning back to FIG. 1a, the present invention can also be
employed in connection with asset management. Typically diagnostics
activities for many industrial and commercial organizations are conducted
separate from control and process operation activities. In addition, the
interface to acquire needed maintenance and repair components is often
done manually. Similarly, capital acquisition of replacement equipment is
also performed in a manual, batch, off-line manner. Equipment acquisition
decisions are often made with a separate economic analysis including
pricing analysis and consideration for capital funding available. It is
difficult to incorporate dynamic operational data such as efficiency,
reliability, and expected maintenance cost into this analysis. The
growing presence of e-commerce and computer-accessible acquisition
information is rarely utilized by computer systems. Instead, these
e-commerce systems are often accessed by a human. The subject invention
includes an optimization function that facilitates realization of maximum
revenue from an industrial machine while mitigating catastrophic failure.
Machinery operation can be altered as needed to run less efficiently or
noisier as needed to maintain useful machinery operation.

[0154]Thus the subject invention integrates the aforementioned
optimization functionality with asset management and logistics systems
such as e-commerce systems. Such tightly integrated approach can enable a
process to predict a failure, establish when a replacement component
could be delivered and installed, and automatically alter the control to
insure continued operation until the replacement part arrives. For
example, a needed replacement part could automatically be ordered and
dynamically tracked via the Internet to facilitate continued operation.
Alterations in the control could automatically be made based on changes
in an expected delivery date and prognostic algorithms results. For
example, a prognostic algorithm could determine a drive-end bearing
system has degraded and has perhaps 500 operating hours left at the
current speeds, loads, and temperatures. The correct needed replacement
bearing could be automatically ordered via an e-commerce web site (e.g.
PTPlace) and shipment tracked until the part arrived. The control may be
automatically altered to extend the useful life of the bearing as
required (e.g. reducing speed by 1/2 doubles the bearing life). Delays in
receiving the needed replacement could cause the part to be ordered from
another source and the control dynamically altered as needed. Maintenance
could be scheduled to replace the part to coincide with the part arrival.

[0155]In the case of excessive maintenance costs, the optimization program
could determine that continually replacing failing components is not
longer an optimum strategy and could perform an economic analysis on a
new more reliable component or a new machine. The new machine could
provide a far more optimum solution than continually running in a
degraded condition and replacing individual components. The new
replacement machine (e.g. a motor) could be automatically ordered and
scheduled to swap out the older, high-maintenance item. Optimization
techniques that optimize the design and selection of components could be
integrated with real-time dynamic optimization and integrated with
internet-based product information and ordering information to provide a
superior level of process optimization as compared to conventional asset
management schemes.

[0156]In view of the exemplary systems shown and described above,
methodologies that may be implemented in accordance with the present
invention will be better appreciated with reference to the flow diagram
of FIG. 8. While, for purposes of simplicity of explanation, the
methodology is shown and described as a series of blocks, it is to be
understood and appreciated that the present invention is not limited by
the order of the blocks, as some blocks may, in accordance with the
present invention, occur in different orders and/or concurrently with
other blocks from that shown and described herein. Moreover, not all
illustrated blocks may be required to implement the methodology in
accordance with the present invention.

[0157]The invention may be described in the general context of
computer-executable instructions, such as program modules, executed by
one or more components. Generally, program modules include routines,
programs, objects, data structures, etc. that perform particular tasks or
implement particular abstract data types. Typically the functionality of
the program modules may be combined or distributed as desired in various
embodiments.

[0158]FIG. 8 is a high-level flow diagram depicting one particular
methodology 800 in connection with facilitating optimizing an industrial
automation system in accordance with the subject invention. At 810, data
relating to machine diagnostics or prognostics is received. The data can
be collected from a historical database, collected in situ for example
from operation of the various machines, collected via various sensing
devices, and generated via analyzing the aforementioned collected data.
The generated data can also relate to future predicted states of the
respective machines and/or with respect to clusters of the machines.

[0159]The data can be obtained for example via measuring an attribute
associated with a motorized system (e.g., motorized pump, fan, conveyor
system, compressor, gear box, motion control device, screw pump, and
mixer, hydraulic or pneumatic machine, or the like). The measured
attribute may comprise, for example, vibration, pressure, current, speed,
and/or temperature associated with the motorized system. The data can
comprise data relating to the health of the motorized system according to
the measured attribute. For example, diagnostics data can be generated
which may be indicative of the diagnosed motorized system health, whereby
the motorized system is operated according to a setpoint and/or the
diagnostics data generated. The provision of the diagnostics data may
comprise, for example, obtaining a frequency spectrum of the measured
attribute and analyzing the frequency spectrum in order to detect faults,
component wear or degradation, or other adverse condition in the
motorized system, whether actual or anticipated. The diagnosis may
further comprise analyzing the amplitude of a first spectral component of
the frequency spectrum at a first frequency.

[0160]In order to provide the diagnostics data, the invention may provide
the measured attribute(s) to a neural network, an expert system, a fuzzy
logic system, and/or a data fusion component, or a combination of these,
which generates the diagnostics signal indicative of the health of the
motorized system. For example, such frequency spectral analysis may be
employed in order to determine faults or adverse conditions associated
with the system or components therein (e.g., motor faults, unbalanced
power source conditions, etc.). In addition, the diagnosis may identify
adverse process conditions, such as cavitation in a motorized pumping
system.

[0162]At 830 and 840, the data is analyzed in connection with optimization
software that analyzes the machine data as well as the business concern
data. Such analysis can include searching for and identifying
correlations amongst the data, trend analysis, inference analysis, data
mining, data fusion analysis, etc. in an effort to identify schemes for
reorganizing, restructuring, modifying, adding and/or deleting the
various machine and business components so as to facilitate optimizing
the overall business system or method in accordance with identified
business objective(s).

[0163]At 850, a determination is made as to whether component or system
reconfiguration may result in convergence toward optimization. If YES,
the system is reconfigured in a manner coincident with a predicted
configuration expected to achieve a more desired end result. If, NO, the
process returns to 810.

[0164]At 860, a determination is made as to whether the system has been
optimized. If NO, the process returns to 640. If YES, the process returns
to 810.

[0165]The following discussion with reference to FIGS. 9-20 provides
additional detail as to exemplary systems and methods for collecting and
analyzing machine data in connection with the subject invention. It is to
be appreciated that such discussion is merely provided to ease
understanding of the subject invention, and not to limit the invention to
such systems and methods. In FIG. 9, an exemplary motorized pump system
902 is illustrated having a pump 904, a three phase electric motor 906,
and a control system 908 for operating the system 902 in accordance with
a setpoint 910. While the exemplary motor 906 is illustrated and
described herein as a polyphase synchronous electric motor, the various
aspects of the present invention may be employed in association with
single-phase motors as well as with DC and other types of motors. In
addition, the pump 904 may comprise a centrifugal type pump, however, the
invention finds application in association with other pump types not
illustrated herein, for example, positive displacement pumps.

[0166]The control system 908 operates the pump 904 via the motor 906
according to the setpoint 910 and one or more measured process variables,
in order to maintain operation of the system 902 commensurate with the
setpoint 910 and within allowable process operating ranges specified in
setup information 968, supplied to the control system 908 via a user
interface 911. For example, it may be desired to provide a constant fluid
flow, wherein the value of the setpoint 910 is a desired flow rate in
gallons per minute (GPM) or other engineering units. The setup
information 968, moreover, may comprise an allowable range of operation
about the setpoint 910 (e.g., expressed in GPM, percentage of process
variable span, or other units), and allowable range of operation for
other process and machinery parameters such as temperature, pressure, or
noise emission, wherein the control system 908 may operate the system 902
at an operating point within the allowable range.

[0167]Alternatively or in combination, setup information, setpoints, and
other information may be provided to the control system 908 by a user 912
via a computer 913 operatively connected to a network 914, and/or by
wireless communications via a transceiver 915. Such information may be
provided via the network 914 and/or the wireless communications
transceiver 915 from a computer (e.g., computer 913) and/or from other
controllers such as a programmable logic controller (PLC, not shown) in a
larger process, wherein the setpoint 910, setup information, and/or one
or more economic values 916 (e.g., related to or indicative of energy
costs, which may vary with time, peak loading values, and current loading
conditions, material viscosity values, and the like) are provided to the
control system 908, as illustrated and described in greater detail
hereinafter. The control system 908, moreover, may include a modem 917
allowing communication with other devices and/or users via a global
communications network, such as the Internet 918, whereby such setpoint,
setup, performance, and other information may be obtained or provided to
or from remote computers or users. In this regard, it will be appreciated
that the modem 917 is not strictly required for Internet or other network
access.

[0168]The pump 904 comprises an inlet opening 920 through which fluid is
provided to the pump 904 in the direction of arrow 922 as well as a
suction pressure sensor 924, which senses the inlet or suction pressure
at the inlet 920 and provides a corresponding suction pressure signal to
the control system 908. Fluid is provided from the inlet 920 to an
impeller housing 926 including an impeller (not shown), which rotates
together with a rotary pump shaft coupled to the motor 906 via a coupling
928. The impeller housing 926 and the motor 906 are mounted in a fixed
relationship with respect to one another via a pump mount 930, and motor
mounts 932. The impeller with appropriate fin geometry rotates within the
housing 926 so as to create a pressure differential between the inlet 920
and an outlet 934 of the pump. This causes fluid from the inlet 920 to
flow out of the pump 904 via the outlet or discharge tube 934 in the
direction of arrow 936. The flow rate of fluid through the outlet 934 is
measured by a flow sensor 938, which provides a flow rate signal to the
control system 908.

[0169]In addition, the discharge or outlet pressure is measured by a
pressure sensor 940, which is operatively associated with the outlet 934
and provides a discharge pressure signal to the control system 908. It
will be noted at this point that although one or more sensors (e.g.,
suction pressure sensor 924, discharge pressure sensor 940, outlet flow
sensor 938, and others) are illustrated in the exemplary system 902 as
being associated with and/or proximate to the pump 904, that such sensors
may be located remote from the pump 904, and may be associated with other
components in a process or system (not shown) in which the pump system
902 is employed. In this regard, other process sensors 941 may be
connected so as to provide signals to the control system 908, for
example, to indicate upstream or downstream pressures, flows, or the
like. Alternatively, flow may be approximated rather than measured by
utilizing pressure differential information, pump speed, fluid
properties, and pump geometry information or a pump model. Alternatively
or in combination, inlet and/or discharge pressure values may be
estimated according to other sensor signals (e.g., 941) and pump/process
information.

[0170]It will be further appreciated that while the motor drive 960 is
illustrated in the control system 908 as separate from the motor 906 and
from the controller 966, that some or all of these components may be
integrated. Thus, for example, an integrated, intelligent motor may be
provided integral to or embedded with the motor 906, to include the motor
drive 960 and the controller 966. Furthermore, the motor 906 and the pump
904 may be integrated into a single unit (e.g., having a common shaft
wherein no coupling 928 is required), with or without an integral control
system (e.g., control system 908, comprising the motor drive 960 and the
controller 966) in accordance with the invention.

[0171]The control system 908 further receives process variable measurement
signals relating to pump temperature via a temperature sensor 942,
atmospheric pressure via a pressure sensor 944 located proximate the pump
904, motor (pump) rotational speed via a speed sensor 946, and vibration
via sensor 948. Although the vibration sensor 948 is illustrated and
described hereinafter as mounted on the motor 906, vibration information
may, alternatively or in combination, be obtained from a vibration sensor
mounted on the pump 906 (not shown). The motor 906 provides rotation of
the impeller of the pump 904 according to three-phase alternating current
(AC) electrical power provided from the control system via power cables
950 and a junction box 952 on the housing of the motor 906. The power to
the pump 904 may be determined by measuring the current and voltage
provided to the motor 906 and computing pump power based on current,
voltage, speed, and motor model information such as efficiency. This may
be measured and computed by a power sensor 954, which provides a signal
related thereto to the control system 908. Alternatively or in
combination, the motor drive 960 may provide motor torque information to
the controller 966 where pump input power is calculated according to the
torque and possibly speed information. Alternatively, input current and
possibly voltage may be measured from the power lines going from the
power source 962 to the motor drive 960 using a sensor 954a. Drive
efficiency and/or motor efficiency equations may be used to determine the
power going into the pump 904. It will be noted that either or both of
the sensors 954 and 954a can be integrated into the motor drive 960.

[0172]The control system 908 also comprises a motor drive 960 providing
three-phase electric power from an AC power source 962 to the motor 906
via the cables 950 in a controlled fashion (e.g., at a controlled
frequency and amplitude) in accordance with a control signal 964 from a
controller 966. The controller 966 receives the process variable
measurement signals from the atmospheric pressure sensor 944, the suction
pressure sensor 924, the discharge pressure sensor 940, the flow sensor
938, the temperature sensor 942, the speed sensor 946, the vibration
sensor 948, the power sensor 954, and other process sensors 941, together
with the setpoint 910, and provides the control signal 964 to the motor
drive 960 in order to operate the pump system 902 commensurate with the
setpoint 910 within specified operating limits. In this regard, the
controller 966 may be adapted to control the system 902 to maintain a
desired fluid flow rate, outlet pressure, motor (pump) speed, torque,
suction pressure, or other performance characteristic.

[0173]Setup information 968 may be provided to the controller 966, which
may include operating limits (e.g., min/max speeds, min/max flows,
min/max pump power levels, min/max pressures allowed, NPSHR values, and
the like), such as are appropriate for a given pump 904, motor 906,
piping and process conditions, and/or process dynamics and other system
constraints. The control system 908 provides for operation within an
allowable operating range about the setpoint 910, whereby the system 902
is operated at a desired operating point within the allowable range, in
order to optimize one or more performance characteristics (e.g., such as
life cycle cost, efficiency, life expectancy, safety, emissions,
operational cost, MTBF, noise, vibration, and the like).

[0174]Referring also to FIG. 10, the controller 966 comprises an
optimization component 970, which is adapted to select the desired
operating point for pump operation within the allowable range about the
setpoint 910, according to an aspect of the invention. As illustrated and
described hereinafter, the optimization component 970 may be employed to
optimize efficiency or other performance characteristics or criteria,
including but not limited to throughput, lifetime, or the like. The
component 970, moreover, may select the desired operating point according
to performance characteristics associated with one or more components in
the system 902 or associated therewith. For example, the optimization
component 970 may generate an optimization signal 972 by correlating
pump, motor, and or motor drive efficiency information associated with
the pump 904, motor 906, and motor drive 960, respectively, to derive a
correlated process efficiency associated with the entire system 902.

[0175]Such component efficiency information may be obtained, for example,
from setup information 969 such as efficiency curves for the pump 904,
motor 906, and drive 960 alone or in combination with such information
derived from one or more of the sensors 924, 938, 940, 941, 942, 944,
946, 954, 954a, and/or 948. In this manner, the efficiency of a
particular component (e.g., pump 904, motor 906, and drive 960) in the
system 902 may be determined from manufacturer data, which may be
supplemented, enhanced, or replaced with actual measured or computed
efficiency information based on prior operation and/or diagnosis of one
or more such components.

[0176]The optimization component 970, moreover, may correlate efficiency
information related to the components of the system 902, along with such
efficiency information related to components of a larger process or
system of which the system 902 is a part, in order to select the desired
operating point for optimization of overall system efficiency. Thus, for
example, the controller 966 may generate the control signal 964 to the
motor drive 960 according to the optimization signal 972 from the
optimization component 970, based on the optimum efficiency point within
the allowable operating range according to the correlated process
efficiency for the system 902. Furthermore, it will be appreciated that
performance information associated with components in unrelated systems
may be employed (e.g., efficiency information related to motors in other,
unrelated systems within a manufacturing facility) in optimizing energy
usage across the entire facility.

[0177]Alternatively or in combination, the controller 966 may operate the
pump within the allowable range about the setpoint 910 in order to
achieve global optimization of one or more performance characteristics of
a larger process or system of which the pump system 902 is a part. Thus,
for example, the components (e.g., pump 904, motor 906, drive 960) of the
system 902 may be operated at less than optimal efficiency in order to
allow or facilitate operation of such a larger process at optimal
efficiency. The controller 966 selectively provides the control signal
964 to the motor drive 960 according to the setpoint 910 (e.g., in order
to maintain or regulate a desired flow rate) as well as to optimize a
performance characteristic associated with the system 902 or a larger
process, via the optimization signal 972. Thus, in one example flow
control is how optimization is achieved in this example. It will be noted
that the allowable range of operation may be provided in lieu of an
actual setpoint, or the allowable range may be derived using the setpoint
value 910.

[0178]In this regard, the controller 966 may provide the control signal
964 as a motor speed signal 964 from a PID control component 974, which
inputs process values from one or more of the sensors 924, 938, 940, 942,
944, 946, 948, 954, and 954a, economic values 916, and the setpoint 910,
wherein the magnitude of change in the control signal 964 may be related
to the degree of correction required to accommodate the present control
strategy, for example, such as system efficiency, and/or the error in
required versus measured process variable (e.g., flow). Although the
exemplary controller 966 is illustrated and described herein as
comprising a PID control component 974, control systems and controllers
implementing other types of control strategies or algorithms (e.g., PI
control, PID with additional compensating blocks or elements,
stochastics, non-linear control, state-space control, model reference,
adaptive control, self-tuning, sliding mode, neural networks, GA, fuzzy
logic, operations research (OR), linear programming (LP), dynamic
programming (DP), steepest descent, or the like) are also contemplated as
falling within the scope of the present invention.

[0179]The exemplary PID component 974 may compare a measured process
variable (e.g., flow rate measured by sensor 938) with the desired
operating point within the allowable range about the setpoint 910, where
the setpoint 910 is a target setpoint flow rate, and wherein one or more
of the process variable(s) and/or the desired operating point (e.g., as
well as the allowable operating range about the setpoint) may be scaled
accordingly, in order to determine an error value (not shown). The error
value may then be used to generate the motor speed signal 964, wherein
the signal 964 may vary proportionally according to the error value,
and/or the derivative of the error, and/or the integral of the error,
according to known PID control methods.

[0180]The controller 966 may comprise hardware and/or software (not shown)
in order to accomplish control of the process 902. For example, the
controller 966 may comprise a microprocessor (not shown) executing
program instructions for implementing PID control (e.g., PID component
974), implementing the efficiency or other performance characteristic
optimization component 970, inputting of values from the sensor signals,
providing the control signal 964 to the motor drive 960, and interacting
with the user interface 911, the network 914, modem 917, and the
transceiver 915. The user interface 911 may allow a user to input
setpoint 910, setup information 968, and other information, and in
addition may render status and other information to the user, such as
system conditions, operating mode, diagnostic information, and the like,
as well as permitting the user to start and stop the system and override
previous operating limits and controls. The controller 966 may further
include signal conditioning circuitry for conditioning the process
variable signals from the sensors 916, 924, 938, 940, 941, 942, 944, 946,
948, and/or 954.

[0181]The controller 966, moreover, may be integral with or separate from
the motor drive 960. For example, the controller 966 may comprise an
embedded processor circuit board mounted in a common enclosure (not
shown) with the motor drive 960, wherein sensor signals from the sensors
916, 924, 938, 940, 941, 942, 944, 946, 948, and/or 954 are fed into the
enclosure, together with electrical power lines, interfaces to the
network 914, connections for the modem 917, and the transceiver 915, and
wherein the setpoint 910 may be obtained from the user interface 911
mounted on the enclosure, and/or via a network, wireless, or Internet
connection. Alternatively, the controller 966 may reside as instructions
in the memory of the motor drive 960, which may be computed on an
embedded processor circuit that controls the motor 906 in the motor drive
960.

[0182]In addition, it will be appreciated that the motor drive 960 may
further include control and feedback components (not shown), whereby a
desired motor speed (e.g., as indicated by the motor speed control signal
964 from the PID component 974) is achieved and regulated via provision
of appropriate electrical power (e.g., amplitude, frequency, phasing,
etc.) from the source 962 to the motor 906, regardless of load
fluctuations, and/or other process disturbances or noise. In this regard,
the motor drive 960 may also obtain motor speed feedback information,
such as from the speed sensor 946 via appropriate signal connections (not
shown) in order to provide closed loop speed control according to the
motor speed control signal 964 from the controller 966. In addition, it
will be appreciated that the motor drive 960 may obtain motor speed
feedback information by means other than the sensor 946, such as through
internally computed speed values, as well as torque feedback information,
and that such speed feedback information may be provided to the
controller 966, whereby the sensor 946 need not be included in the system
902. One control technique where the motor drive 960 may obtain torque
and speed information without sensors is when running in a vector-control
mode.

[0183]As further illustrated in FIG. 11, the optimization component 970
correlates component performance information (e.g., efficiency
information) associated with one or more components (e.g., pump 704,
motor 706, motor drive 760, etc.) in the system 702 in order to derive
correlated process performance information. In addition, the component
970 may employ performance information associated with other components
in a larger process (not shown) of which the system 702 is a part, in
order to derive correlated performance information. It will be
appreciated that the optimization component 970, moreover, may correlate
information other than (or in addition to) efficiency information,
including but not limited to life cycle cost, efficiency, life
expectancy, safety, emissions, operational cost, MTBF, noise, vibration,
and the like.

[0184]The optimization component 970 selects the desired operating point
as the optimum performance point within the allowable range of operation
according to the correlated process performance information. As
illustrated in FIG. 9, the controller 966 may obtain pump efficiency
information 900 related to the pump 704, motor efficiency information 902
related to the motor 706, and motor drive efficiency information 904
related to the motor drive 760, which is provided to a correlation engine
910 in the optimization component 970. The correlation engine 910
correlates the information 900, 902, and/or 904 according to present
operating conditions (e.g., as determined according to values from one or
more of the process sensors 924, 938, 940, 941, 942, 944, 946, 948,
and/or 954, economic value(s) 916, setpoint 910, and allowable operating
range information from setup information 968) in order to determine a
desired operating point within the allowable operating range at which the
efficiency of the system 902 or a larger process (not shown) may be
optimal.

[0185]In this regard, the correlation engine 1110 may compute, predict, or
derive correlated system efficiency information 1112 from the correlation
of one or more of the pump efficiency information 1100 related to the
pump 1104, motor efficiency information 1102 related to the motor 906,
and motor drive efficiency information 904 related to the motor drive
960. The correlation may be accomplished in the correlation engine 1110
through appropriate mathematical operations, for example, in software
executing on a microprocessor within the controller 966. Appropriate
weighting factors may be assigned to the relevant information being
correlated (e.g., 1100, 1102, and 1104), for instance, whereby the
efficiency of the pump 904 may be given more weight than that of the
motor drive 960. The invention can also be employed to provide
near-optimal operation to enhance robustness (e.g., to reduce
sensitivity), in order to provide better overall optimization.

[0186]The correlation engine 1110, moreover, may determine correlated
system efficiency information according to the current operating
conditions of the system 902, such as the process setpoint 910, diagnosed
degradation of system components, etc. Thus, for example, the correlated
system efficiency information 1112 may include different desired
operating points depending on the setpoint 910, and/or according to the
current pressures, flow rates, temperatures, vibration, power usage,
etc., in the system 902, as determined by the values from one or more of
the sensors 924, 938, 940, 941, 942, 944, 946, 948, and/or 954. The
controller 966 then provides the control signal 964 as a motor speed
signal 964 to the motor drive 960 according to the desired operating
point. In addition to efficiency information (e.g., 1100, 1102, 1104) the
component performance information may also comprise one or more of life
cycle cost information, efficiency information, life expectancy
information, safety information, emissions information, operational cost
information, MTBF information, noise information, and vibration
information. The correlation engine 1110 can also comprise algorithms
employing temporal logic. This permits the correlation engine 1110 to
establish dynamic, time varying control signals to optimize system
operation over a time horizon. For example, if energy costs are to rise
during peak daytime periods, the correlation engine may prescribe a
slightly higher throughput during off-peak hours (e.g., less energy
efficient during off-peak hours) in order to minimize operation during
more costly peak energy cost periods.

[0187]FIGS. 12-14 illustrate examples of component performance
characteristic information, which may be correlated (e.g., via the
correlation engine 1110) in order to select the desired operating point
for the system 902. FIG. 12 illustrates a plot of an exemplary pump
efficiency curve 1200 (e.g., related to pump 904), plotted as efficiency
1210 (e.g., output power/input power) versus pump speed 1220. The
exemplary curve 1200 comprises a best operating point 1230, whereat the
pump efficiency is optimal at approximately 62% of maximum rated pump
speed. The pump efficiency information 1100 of the optimization component
970 may comprise one or more such curves, for example, wherein different
curves exist for different flow rates, pressures, temperatures, viscosity
of pumped fluid, etc. Similarly, FIG. 13 illustrates a plot of an
exemplary motor efficiency curve 1300 (e.g., related to motor 906),
plotted as efficiency 1310 (e.g., output power/input power) versus motor
speed 1320. The exemplary curve 1300 comprises a best operating point
1330, whereat the motor efficiency is optimal at approximately 77% of
maximum rated speed.

[0188]It will be appreciated from the curves 1200 and 1300 of FIGS. 12 and
13, respectively, that the optimal efficiency operating points for
individual components (e.g., pump 904 and motor 906) of the system 902,
or of typical motorized systems generally, may not, and seldom do,
coincide. The pump efficiency information 1100 of the optimization
component 970 may comprise one or more such curves 1230 of pump
efficiency versus speed, for example, wherein a different curve exists
for different flow rates, pressures, viscosity of pumped fluid, motor
load, etc. In like fashion, FIG. 14 illustrates a plot of an exemplary
motor drive efficiency curve 1400 (e.g., related to the motor drive 960
of system 902), plotted as efficiency 1410 (e.g., output power/input
power) versus motor (e.g., pump) speed 1420. The exemplary curve 1400
comprises a best operating point 1430, whereat the motor drive efficiency
is optimal at approximately 70% of the rated speed. The motor drive
efficiency information 1104 of the optimization component 970 may
comprise one or more such curves, for example, wherein a different curve
exists for different flow rates, temperatures, torques, pressures,
viscosity of pumped fluid, motor load, motor temperature, etc.

[0189]The correlation engine 1110 of the efficiency optimization component
970 correlates the three curves 1200, 1300, and 1400 in order to derive
correlated system efficiency information 1112. Referring now to FIG. 15,
the correlation engine may correlate the curves 1200, 1300, and 1400 to
derive a correlated system efficiency curve 1500 plotted as system
efficiency optimization 1510 versus speed 1520. The exemplary curve 1500
comprises a peak optimization point 1530 at approximately 71% of rated
speed. This composite performance characteristic curve 1500 may then be
employed by the optimization component 970 in order to select the desired
operation point for the system 902, which may be provided to the PID 974
via the optimization signal 972.

[0190]As illustrated in FIG. 15, where the allowable operating range
includes an upper limit 1540, and a lower limit 1550 (e.g., where these
limits 1540 and 1550 are scaled from process units, such as flow in GPM
into speed), the optimization component 970 may advantageously select the
peak optimization point 1530 at approximately 71% of rated speed, in
order to optimize the efficiency within the allowable operating range. In
another example, where the allowable upper and lower limits 1560 and 1570
are specified, a local optimum 1580 within that range may be selected as
the desired operating point. Many other forms of performance information
and correlations thereof are possible within the scope of the present
invention, beyond those illustrated and described above with respect to
FIGS. 12-15. The preceding discussion described sending a motor speed
signal (e.g., signal 964) to the motor drive 960. Alternatively or in
combination, other drive parameters (e.g., carrier frequency, control
mode, gains, and the like) can be changed, enhanced, modified, etc., in
accordance with the invention. This can enable even more efficient
operation, for example, by changing the efficiency 1500.

[0191]Referring now to FIGS. 16-20, the optimization aspects of the
invention may be employed across a plurality of controllers operating
various actuators (e.g., valves, switches, and the like) and motorized
systems (e.g., pumps, mixers, compressors, conveyors, fans, and the like)
in a large process or system 1600, for optimization of one or more
performance characteristics for unrelated motorized systems. Such
sub-systems may comprise individual controllers, such as valve
controllers, motor controllers, as well as associated motors and drives.
As illustrated in FIG. 16, an integer number N of such individual motor
controllers MC1 through MCN may be networked together via a network 1602,
allowing peer-to-peer communication therebetween, wherein MC1 controls a
motorized pump PUMP1 via a motor M1 and associated motor drive MD1, and
MCN controls a motorized pump PUMPN via a motor MN and associated motor
drive MDN. Other controllers, such as valve controller VC1 may be
connected to the network 1602, and operative to control a valve VALVE1.
It is to be appreciated that that the motor controller may be embedded in
the motor drive such that MC1 and MD1 are one component.

[0193]Another possible configuration is illustrated in FIG. 17, wherein a
host computer 1704 is connected to the network 1702. The host 1704 may
provide centralized operation of the pumps PUMP1 and PUMPN as well as of
the valve VALVE1, for example, by providing setpoint information to the
associated controllers MC1, MCN, and VC1. Other information may be
exchanged between the computer 1704 and the various controllers MC1, MCN,
and VC1 in host-to-peer fashion, such as information relating to process
conditions, control information, and performance characteristic
information, whereby an efficiency optimization component 1706 in the
host computer 1704 may determine desired operating points for one or more
of the controllers MC1, MCN, and VC1 according to one or more performance
characteristics associated with the system 1700. Alternatively or in
combination, one or more of the individual controllers MC1, MCN, and VC1
may determine desired operating points for the associated sub-systems
according to performance characteristic information obtained from the
host computer 1704, from other controllers via the network 1702, and/or
from sensors associated with the individual sub-systems.

[0194]Referring now to FIG. 18, another process 1500 is illustrated for
providing material from first and second tanks TANK1 and TANK2 into a
mixing tank TANK3 via pumps PUMP1 and PUMP2 with associated motors,
drives and controllers. The material is mixed in TANK3 via a motorized
mixer with associated motor M3, drive MD3, and controller MC3. Mixed
material is then provided via a motorized pump PUMP3 and control valve
VALVE1 to a molding machine 1502 with an associated motor M5, whereafter
molded parts exit the machine 1502 via a chute 1504 to a motorized
conveyor 1506 controlled by motor M6, which transports the molded parts
to a cooler device 1508 having a motorized compressor 1510. The cooled
parts are then provided to a second motorized conveyor 1512 whereat a
motorized fan facilitates removal of moisture from the parts.

[0195]The various motor and valve controllers MC1-MC9 and VC1 associated
with the various sub-systems of the process 1500 are networked together
via a network 1520 in order to provide peer-to-peer or other types of
communications therebetween. One or more of these controllers MC1-MC9 and
VC1 may be adapted to correlate performance characteristic information
associated with component devices (e.g., motors, drives, valves) in order
to determine desired operating points for one, some, or all of the
sub-systems in the process 1500 in accordance with the invention.

[0196]A host computer 1532, moreover, may be provided on the network 1520,
which may comprise an optimization component 1532 operative to determine
desired operating points (e.g., as well as setpoints, allowable operating
ranges about such setpoints, and the like) for one or more of the
sub-systems in the process 1500 according to one or more performance
characteristics associated with the process 1500, which may be then
communicated to the various controllers MC1-MC9 and VC1 in order to
optimize performance of the process 1500 in some aspect (e.g.,
efficiency, cost, life cycle cost, throughput, efficiency, life
expectancy, safety, emissions, operational cost, MTBF, noise, vibration,
and the like). Thus, in accordance with the present invention, the
process 1500 may be operated to both produce molded parts from raw
materials, and at the same time to optimize one or more performance
metrics, such as cost per part produced. Operation of the system may be
controlled such that prognostic information regarding machinery failure,
expected delivery of repair parts, and expected energy costs is
considered in defining an optimum operating mode. For example, if the
molding machine is predicted to fail in one week, then increased
work-in-process inventory may be generated while the needed repair parts
are automatically ordered and delivery expedited. Alternatively a more
optimum control mode may be to operate the molding machine very slow and
slow down other process equipment to maintain a lower production rate but
a continuous flow of finished products.

[0197]Another aspect of the invention provides a methodology by which a
motorized system may be controlled. The methodology comprises selecting a
desired operating point within an allowable range of operation about a
system setpoint according to performance characteristics associated with
one or more components in the system, and controlling the system
according to the desired operating point. The selection of the desired
operating point may include correlating component performance information
associated with one or more components in the system in order to derive
correlated system performance information, and selecting the desired
operating point as the optimum performance point within the allowable
range of operation according to the correlated system performance
information. The performance information, setpoint, and/or the allowable
operating range may be obtained from a user or another device via a user
interface, via a host computer or other controller through a network, via
wireless communications, Internet, and/or according to prior operation of
the system, such as through trend analysis.

[0198]An exemplary method 1900 is illustrated in FIG. 19 for controlling a
motorized system in accordance with this aspect of the invention. While
the exemplary method 1900 is illustrated and described herein as a series
of blocks representative of various events and/or acts, the present
invention is not limited by the illustrated ordering of such blocks. For
instance, some acts or events may occur in different order and/or
concurrently with other acts or events, apart from the ordering
illustrated herein, in accordance with the invention. Moreover, not all
illustrated blocks, events, or acts, may be required to implement a
methodology in accordance with the present invention. In addition, it
will be appreciated that the exemplary method 1900, as well as other
methods according to the invention, may be implemented in association
with the pumps and systems illustrated and described herein, as well as
in association with other motorized systems and apparatus not illustrated
or described, including but not limited to fans, conveyor systems,
compressors, gear boxes, motion control devices, screw pumps, mixers, as
well as hydraulic and pneumatic machines driven by motors or turbo
generators.

[0199]Beginning at 1902, the method 1900 comprises obtaining a system
setpoint at 1904, and obtaining an allowable operating range at 1906. The
setpoint and operating range may be obtained at 1904 and 1906 from a user
or a device such as a controller, a host computer, or the like, via a
user interface, a network, an Internet connection, and/or via wireless
communication. At 1908, component performance information is obtained,
which may be related to components in the system and/or components in a
larger process of which the controlled system is a part. Component
performance information may be obtained from vendor data, from e-commerce
web sites, from measured historical data, or from simulation and modeling
or any combination of this these. The component performance information
is then correlated at 1910 in order to derive correlated system
performance information. At 1912, a desired operating point is selected
in the allowable operating range, according to the correlated system
performance information derived at 1910. The system is then controlled at
1914 according to the desired operating point, whereafter the method 1900
returns to 1908 as described above. Process changes, disturbances,
updated prognostic information, revised energy costs, and other
information may require periodic evaluation and appropriate control
adjustment in order to ensure meeting optimum performance levels as the
process changes (e.g., tanks empty, temperature changes, or the like) and
optimizing asset utilization.

[0200]Another aspect of the invention provides for controlling a motorized
system, such as a pump, wherein a controller operatively associated with
the system includes a diagnostic component to diagnose an operating
condition associated with the pump. The operating conditions detected by
the diagnostic component may include motor, motor drive, or pump faults,
pump cavitation, pipe breakage or blockage, broken impeller blades,
failing bearings, failure and/or degradation in one or more system
components, sensors, or incoming power, and the like. The controller
provides a control signal to the system motor drive according to a
setpoint and a diagnostic signal from the diagnostic component according
to the diagnosed operating condition in the pump. The diagnostic
component may perform signature analysis of signals from one or more
sensors associated with the pump or motorized system, in order to
diagnose the operating condition. Thus, for example, signal processing
may be performed in order to ascertain wear, failure, or other
deleterious effects on system performance, whereby the control of the
system may be modified in order to prevent further degradation, extend
the remaining service life of one or more system components, or to
prevent unnecessary stress to other system components. In this regard,
the diagnostic component may process signals related to flow, pressure,
current, noise, vibration, temperature, and/or other parameters of
metrics associated with the motorized system. Such a system will be able
to effectively control the remaining useful life of the motorized system.

[0201]Referring now to FIG. 20, another exemplary pump system 2002 is
illustrated, in which one or more aspects of the invention may be carried
out. The system 2002 includes a pump 2004, a three phase electric motor
2006, and a control system 2008 for operating the system 2002 in
accordance with a setpoint 2010. While the exemplary motor 2006 is
illustrated and described herein as a polyphase synchronous electric
motor, the various aspects of the present invention may be employed in
association with single-phase motors as well as with DC and other types
of motors. In addition, the pump 2004 may comprise a centrifugal type
pump, however, the invention finds application in association with other
pump types not illustrated herein, for example, positive displacement
pumps. Additionally other motor-driven equipment such as centrifugal
compressors, reciprocating compressors, fans, motor-operated valves and
other motor driven equipment can be operated with a controller in a
dynamic environment.

[0202]The control system 2008 operates the pump 2004 via the motor 2006
according to the setpoint 2010 and one or more measured process
variables, in order to maintain operation of the system 2002 commensurate
with the setpoint 2010 and within the allowable process operating ranges
specified in setup information 2068, supplied to the control system 2008
via a user interface 2011. For example, it may be desired to provide a
constant fluid flow, wherein the value of the setpoint 2010 is a desired
flow rate in gallons per minute (GPM) or other engineering units. The
setup information 2068, moreover, may comprise an allowable range of
operation about the setpoint 2010 (e.g., expressed in GPM, percentage of
process variable span, or other units), wherein the control system 2008
may operate the system 2002 at an operating point within the allowable
range.

[0203]Alternatively or in combination, setup information, setpoints, and
other information may be provided to the control system 2008 by a user
2012 via a host computer 2013 operatively connected to a network 2014,
and/or by wireless communications via a transceiver 2015. Such
information may be provided via the network 2014 and/or the wireless
communications transceiver 2015 from a host computer (e.g., computer
2013) and/or from other controllers (e.g., PLCs, not shown) in a larger
process, wherein the setpoint 2010, and/or setup information are provided
to the control system 2008, as illustrated and described in greater
detail hereinafter. The control system 2008, moreover, may include a
modem 2017 allowing communication with other devices and/or users via a
global communications network, such as the Internet 2018.

[0204]The pump 2004 comprises an inlet opening 2020 through which fluid is
provided to the pump 2004 in the direction of arrow 2022 as well as a
suction pressure sensor 2024, which senses the inlet or suction pressure
at the inlet 2020 and provides a corresponding suction pressure signal to
the control system 2008. Fluid is provided from the inlet 2020 to an
impeller housing 2026 including an impeller (not shown), which rotates
together with a rotary pump shaft coupled to the motor 2006 via a
coupling 2028. The impeller housing 2026 and the motor 2006 are mounted
in a fixed relationship with respect to one another via a pump mount
2030, and motor mounts 2032. The impeller with appropriate fin geometry
rotates within the housing 2026 so as to create a pressure differential
between the inlet 2020 and an outlet 2034 of the pump 2004. This causes
fluid from the inlet 2020 to flow out of the pump 2004 via the outlet or
discharge tube 2034 in the direction of arrow 2036. The flow rate of
fluid through the outlet 2034 is measured by a flow sensor 2038, which
provides a flow rate signal to the control system 2008.

[0205]In addition, the discharge or outlet pressure is measured by a
pressure sensor 2040, which is operatively associated with the outlet
2034 and provides a discharge pressure signal to the control system 2008.
It will be noted at this point that although one or more sensors (e.g.,
suction pressure sensor 2024, discharge pressure sensor 2040, outlet flow
sensor 2038, and others) are illustrated in the exemplary system 2002 as
being associated with and/or proximate to the pump 2004, that such
sensors may be located remote from the pump 2004, and may be associated
with other components in a process or system (not shown) in which the
pump system 2002 is employed. In this regard, other process sensors 2041
may be connected so as to provide signals to the control system 2008, for
example, to indicate upstream or downstream pressures, flows,
temperatures, levels, or the like. Alternatively, flow may be
approximated rather than measured by utilizing differential pressure
information, pump speed, fluid properties, and pump geometry information
or a pump model (e.g., CFD model). Alternatively or in combination, inlet
and/or discharge pressure values may be estimated according to other
sensor signals (e.g., 2041) and pump/process information.

[0206]In addition, it will be appreciated that while the motor drive 2060
is illustrated in the control system 2008 as separate from the motor 2006
and from the controller 2066, that some or all of these components may be
integrated. Thus, for example, an integrated, intelligent motor may be
provided with the motor 2006, the motor drive 2060 and the controller
2066. Furthermore, the motor 2006 and the pump 2004 may be integrated
into a single unit (e.g., having a common shaft wherein no coupling 2028
is required), with or without integral control system (e.g., control
system 2008, comprising the motor drive 2060 and the controller 2066) in
accordance with the invention.

[0207]The control system 2008 further receives process variable
measurement signals relating to pump temperature via a temperature sensor
2042, atmospheric pressure via a pressure sensor 2044 located proximate
the pump 2004, motor (pump) rotational speed via a speed sensor 2046, and
vibration via sensor 2048. The motor 2006 provides rotation of the
impeller of the pump 2004 according to three-phase alternating current
(AC) electrical power provided from the control system via power cables
2050 and a junction box 2052 on the housing of the motor 2006. The power
to the pump 2004 may be determined by measuring the current provided to
the motor 2006 and computing pump power based on current, speed, and
motor model information. This may be measured and computed by a power
sensor 2054 or 2054A, which provides a signal related thereto to the
control system 2008. Alternatively or in combination, the motor drive
2060 may provide motor torque information to the controller 2066 where
pump input power is calculated according to the torque and possibly speed
information and motor model information.

[0208]The control system 2008 also comprises a motor drive 2060 providing
three-phase electric power from an AC power source 2062 to the motor 2006
via the cables 2050 in a controlled fashion (e.g., at a controlled
frequency and amplitude) in accordance with a control signal 2064 from a
controller 2066. The controller 2066 receives the process variable
measurement signals from the atmospheric pressure sensor 2044 (2054a),
the suction pressure sensor 2024, the discharge pressure sensor 2040, the
flow sensor 2038, the temperature sensor 2042, the speed sensor 2046, the
vibration sensor 2048, the power sensor 2054, and other process sensors
2041, together with the setpoint 2010, and provides the control signal
2064 to the motor drive 2060 in order to operate the pump system 2002
commensurate with the setpoint 2010. In this regard, the controller 2066
may be adapted to control the system 2002 to maintain a desired fluid
flow rate, outlet pressure, motor (pump) speed, torque, suction pressure,
tank level, or other performance characteristic.

[0209]Setup information 2068 may be provided to the controller 2066, which
may include operating limits (e.g., min/max speeds, min/max flows,
min/max pump power levels, min/max pressures allowed, NPSHR values, and
the like), such as are appropriate for a given pump 2004, motor 2006, and
piping and process conditions. The controller 2066 comprises a diagnostic
component 2070, which is adapted to diagnose one or more operating
conditions associated with the pump 2004, the motor 2006, the motor drive
2060, and/or other components of system 2002. In particular the
controller 2066 may employ the diagnostic component 2070 to provide the
control signal 2064 to the motor drive 2060 according to setpoint 2010
and a diagnostic signal (not shown) from the diagnostic component 2070
according to the diagnosed operating condition in the pump 2004 or system
2002. In this regard, the diagnosed operating condition may comprise
motor or pump faults, pump cavitation, or failure and/or degradation in
one or more system components. The controller 2066 may further comprise
an optimization component 2070a, operating in similar fashion to the
optimization component 70 illustrated and described above.

[0210]The diagnostic component may advantageously perform signature
analysis of one or more sensor signals from the sensors 2024, 2038, 2040,
2041, 2042, 2044, 2046, 2048, and/or 2054, associated with the pump 2004
and/or the system 2002 generally, in order to diagnose one or more
operating conditions associated therewith. Such signature analysis may
thus be performed with respect to power, torque, speed, flow, pressure,
and other measured parameters in the system 2004 of in a larger process
of which the system 2002 is a part. In addition, the signature analysis
may comprise frequency analysis employing Fourier transforms, spectral
analysis, space vector amplitude and angular fluctuation, neural
networks, data fusion techniques, model-based techniques, discrete
Fourier transforms (DFT), Gabor transforms, Wigner-Ville distributions,
wavelet decomposition, non-linear filtering based statistical techniques,
analysis of time series data using non-linear signal processing tools
such as Poincare' maps and Lyapunov spectrum techniques, and other
mathematical, statistical, and/or analytical techniques. The diagnostic
features of the component 2070, moreover, may be implemented in hardware,
software, and/or combinations thereof in the controller 2066.

[0211]Such techniques may be used to predict the future state or health of
components in the system 2002 (e.g., and/or those of a larger system of
which system 2002 is a part or with which system 2002 is associated).
This prognostics will enable the control to be altered to redistribute
stress, to control the time to failure, and/or the remaining useful life
of one or more such components or elements. It will be appreciated that
such techniques may be employed in a larger system, such as the system
300 of FIG. 10, for example, wherein a known or believed good component
or sub-system may be overstressed to allow another suspected weakened
component to last longer.

[0212]FIG. 21 provides further illustration 2100 of enterprise resource
planning (ERP) component 184 that, in accordance with aspects of the
claimed subject matter, can facilitate and/or effectuate utilization of
predictive enterprise manufacturing intelligence (EMI) facilities in
order to provide the ability to conceptualize and display current,
scheduled, forecasted, potentially possible, hypothetical, and/or
predicted process conditions. As illustrated, enterprise resource
planning component 184 can include capacity management component 2102,
energy optimization component 2104, and profit optimization component
2106. In relation to enterprise resource planning component 184 since
much, though not all, of the configuration and operation of this
component is substantially similar to that described in relation to FIGS.
1a-1k, and FIG. 1k in particular, a detailed description of such
features, unless where necessary, has been omitted for the sake of
brevity and to avoid needless prolixity.

[0213]Capacity management component 2102 can leverage process models to
visually present real-time, dynamic comparisons of a process' theoretical
capacity and its current production rate. Capacity management component
2102 can provide timely visibility into potential capacity from existing
factors of production (e.g., resources employed to produce goods and/or
services) thereby avoiding latency of decisions. Capacity management
component 2102 can perform dynamic constraint profiling based at least in
part on current and/or predicted operating conditions, and by linking
into a corporate business system, can automatically quantify the
potential gains of increased capacity as a result of driving production
up to prevailing constraints. The potential gains can be further
characterized as a probability or likelihood measure of potential
economic gain.

[0214]Additionally, capacity management component 2102 can contain or
utilize a built-in framework for instantaneous analysis of potential
scenarios to achieve optimal capacity by product, shift, and/or diverse
and disparate production site. This functionality can allow plant
facility management the ability to analyze tradeoffs associated with the
multiple choices available to achieve optimal production, resulting in
faster and more accurate and timely capture of business opportunities
from improved decision making.

[0215]As those reasonably cognizant in this field of endeavor will no
doubt be aware, today production analysis is typically based on
historical data and user-defined spreadsheets. In some cases, data mining
tools can be employed in conjunction with real-time or near-real time
data from control infrastructure, yet this technique is inherently
retrospective and its value is limited to understanding what happened. In
contrast, capacity management component 2102, in conjunction with various
aspects of enterprise resource planning component 184, leverages
predictive technologies and integrates financial variables with high
fidelity models that can be utilized to control processes, to provide
users the ability to understand the economic value of opportunities as
these unfold, and the ability to capture profitable opportunities or shed
non-profitable opportunities proactively and with a greater degree of
confidence.

[0216]Moreover, as those of reasonable skill in this field of endeavor
will be equally aware, production facilities can make significant
investments in capital improvement projects, aiming to streamline
production and identifying and resolving bottlenecks in manufacturing
units using anecdotal evidence based at least in part on a plant or
production facility's historical performance. Often, for example, a major
capital asset is replaced with the expectation that the removal of this
prevailing constraint will result in production improvements, only to
learn that the achieved improvement is of minimal or marginal benefit
because the available capacity to the next constraint is miniscule.
Capacity management component 2102, in concert with and through
utilization of the disparate and various capabilities associated with
enterprise resource planning component 184, can automatically determine
or identify a facility's top constraints (e.g., top 5, 10, 20, . . . ,
constraints) and quantifies the latent capacity available across these
identified constraints, providing operations management with financial
profiles of production opportunities restricted by these constraints.
Capacity management component 2102 can thus allow for capital expenditure
planning with a greater degree of confidence, having a thorough
understanding of the potential economic improvements associated with
de-bottlenecking projects.

[0217]Energy optimization component 2104 in order to present
visualizations of economic optima that meet a plant or production
facility's predicted energy demand can, together with modeling frameworks
and disparate predictive capabilities, utilize multiple sub-models of
production, utilities, and emissions integrated with a plant or
production facility's (or business entities) financial system. Energy
optimization component 2104 can create an integrated energy-supply model
by incorporating the variable costs associated with an entities business
systems, economic sub-models can be constructed for each
energy-generating asset at a production facility in order to determine
each asset's financial profile, taking into account their generating
capacity, efficiency curves, reliability, and operating costs. Each of
these asset sub-models can be combined to create a production facility's
holistic energy-supply model.

[0218]Additionally, energy optimization component 2104 can create the
production facility's energy-demand model by leveraging powerful
optimization or predictive engines. From the created energy-demand model,
sub-models of production can be developed in order to determine, at user
defined time horizons, predicted energy demands based at least in part on
current and prospective operating objectives. Further, energy
optimization component 2104 can integrate the developed energy-supply and
energy-demand models to produce an energy optimization model. The
integration of the developed energy-supply and energy-demand models can
be integrated using a modeling framework to solve economic supply optima
and expose the most cost-effective energy-generating assets available to
meet predicted demand. For enterprises that operate under green
initiatives or corporate sustainability programs, energy optimization
component 2104 can, for instance, integrate a model of each asset's
emissions thereby ensuring that the economic optimum incorporates the
environmental impact associated with meeting the production facility's
energy demand. This model can be further expanded to include a
probabilistic estimation components, sensitivity analysis components, and
adaptive modeling components. The probabilistic component can, for
example, maximize the certainty of achieving a level of economic benefit
or financial return on an investment. The sensitivity analysis component
can identify factors and operating strategies that while showing
excellent results, can be brittle and can suffer from the effects of
unmodeled disturbances or events that can potentially take place. The
adaptive modeling component can continually assess the impact of
historical decisions and use this information to generate model structure
or parameter changes, to establish causal relationships that can exist in
the model, to improve the stochastic measures assigned to outcomes, or to
generate additional rules or heuristics for future economic analysis and
decision making functions.

[0219]It should be noted without limitation or loss of generality that
developed or created models can be integrated by energy optimization
component 2104 in series, parallel, nested, or in a networked structure
to provide the most efficient solution to attain an economic objective.
The goal of energy optimization component 2104 is to provide timely
visibility into the most cost-effective source of energy to meet the
predicted demand from production, while ensuring full environmental
compliance. Accordingly, energy optimization component 2104 can contain
built-in decision support frameworks for instantaneous analysis of
potential scenarios for decision support. Production facilities with
available third party sources of energy can thus incorporate the
financial parameters (e.g., scheduling production runs during lower cost
off peak energy windows, etc.) of their supply contracts to support make
vs. buy decisions based at least in part on the production facilities
predicted demand. The system can generate a set of potential scenarios
and establish their potential benefit. The system can operate in a
generative mode and sequentially establish new operating scenarios in a
manner that progressively provide increased economic value and return on
the investment. Various search and optimization methods such as the
gradient search method previously presented can be used. Further, the
expected supply, demand, and economic value can be interpreted in the
context of a stochastic system. Likelihood estimates can be made based at
least in part on historical data or other statistical modeling schemes.

[0220]The value of utilizing energy optimization component 2104,
previously described, is to meet a production facility's energy demand at
the lowest possible cost while achieving production objectives and
balancing environmental emissions. As will be appreciated, the high cost
of energy has become the number one concern to manufactures across the
globe, with no signs of abatement. Understanding the impact of energy
usage at production facilities dispersed around the world must
necessarily go beyond anecdotal analysis of past performance, and
real-time consumption monitoring generally only allows for reactive
decision making to curtail the cost of energy. Additionally,
manufacturers often find themselves rushing to meet energy demands from
production by sourcing energy without full knowledge of the economic
impact to the organization's profitability. The environmental effects
caused by surges in energy production are also typically known after the
fact, risking emissions violations and possibly tarnishing the
organization's corporate image with local communities, while the true
cost to operations is only known once the financial books close well
after the end of the fiscal month.

[0221]Through utilization of energy optimization component 2104, and in
particular, by leveraging the predictive capabilities of energy
optimization component 2104 and integrating financial variables into a
modeling framework, energy optimization component 2104 can provide
manufacturers with the ability to understand the economic balance between
the energy demand necessary to meet production objectives and their
production facility's energy supply capability, ensuring a greater degree
of confidence in their decisions.

[0222]Moreover, by simultaneously profiling the different energy scenarios
that can be present by energy optimization component 2104 manufacturers
and more particularly production facility managers can proactively
determine the most cost-effective asset configurations in order to
achieve their production facility's energy demand while achieving
production targets and still keeping environmental emissions in check.
For instance, energy optimization component 2104 can be employed in
campus energy management where visualizations of how many people will be
in particular buildings, weather forecasting, etc., can provide rich
insights into what future energy consumption will look like. Moreover,
models that are developed by, or for, energy optimization component 2104,
or for that matter, models constructed by, or for, other aspects of the
claimed matter (e.g., capacity management component 2102 or profit
optimization component 2106) can be utilized interchangeably by any other
component aspect of the claimed matter, and further are dynamic in
nature. The energy optimization component 2104 can be augmented with a
scenario search component that can generate a series of possible
operating scenarios. The resultant likely economic impact and probability
of achieving this economic impact can be evaluated. Scenarios can be
progressively chosen to exploit or pursue a strategy that provides a more
global optimum. In addition to the expected economic benefit, also
associated with each scenario is the time and cost required to realize
the target scenario and the stability or brittleness of the scenario. For
example, a scenario with high economic benefit may be difficult to
sustain due to external disturbances or may preclude transitioning to a
more optimum scenario with out additional cost, delay, or downtime.
Alternatively, the scenario search method can uncover an unlikely
scenario that meets all the energy and production constraints in an
optimum manner. Such a strategy can involve operating the system in a
unique manner that would have not been discovered by traditional
production planning methods.

[0223]Profit optimization component 2106 can utilize data and information
supplied by capacity management component 2102 and/or energy optimization
component 2104 as well as data and information from a multiplicity of
disparate other sources such as financial variables, quality components,
supplier data, historical performance data, and the like. Profit
optimization component 2106, based at least in part on the supplied data
and information, can thereafter perform margin optimization. For
instance, profit optimization component 2106, where the process involves
fabricating product X, can employ information related to contracts and
product schedules to analyze variable costs (e.g., energy, additives,
feedstock costs, . . . ) in order to optimize profitability. It should be
noted that profit optimization component 2106 can utilize financial
information in a dynamic manner rather than in a static manner, and
further can factor inefficiencies of equipment, equipment life-cycle,
down-time, repair, retooling, labor cost, and the like. Profit
optimization component 2106, like capacity management component 2102 and
energy optimization component 2104, can leverage predictive technologies
to optimize profits. Moreover, profit optimization component 2106 can
also employ look ahead key performance indicators (KPIs) associated with
a process or an enterprise's month-end or year-end goals to maximize
profits. Additionally, profit optimization component 2106 can analyze
historical opportunity costs as well as profit velocity (e.g., how fast a
certain profit can be made and how soon it can be made) in order to learn
how to drive future decision making. Furthermore, profit optimization
component 2106 can also include a currency arbitrage feature that can be
utilized to optimize profitability. In exercising this currency arbitrage
feature, profit optimization component 2106 can consider the costs of
goods and/or services available based at least in part on different world
currencies, locations of availability, shipment costs, production
scenarios, and the like. Furthermore, profit optimization component 2106
can include a variety financial models including option pricing models
that consider making a relatively small near-term investment that
provides the option of making a more substantial investment for economic
benefit sometime in the future when more information is known or there is
greater certainty of achieving the target return on the investment. The
profit optimization component 2106 also includes a stochastic model of
the operating scenario and external economic factors such as interest
rate, labor rates, cost of capital, including international economic
factors that will influence business. Other factors such as variability
in demand and machinery reliability such as probability of failure in a
given time period given a particular equipment loading rate and
maintenance activity. This can permit balancing risk-benefit conditions
to match the operating and investment strategy of the organization.

[0224]Turning now to FIG. 22 which further illustrates 2200 the various
and disparate aspects and components that can be used in conjunction with
capacity management component 2102, energy optimization component 2104,
and/or profit optimization component 2106, and that are integral aspects
of enterprise resource planning (ERP) component 184. As illustrated
enterprise resource planning component 184 can include advisory component
2202 that can utilize a decision-support framework, such as prognostics
engine 110 or optimization engine 2210 (described infra), interpolated
data as a function of historical data as well as knowledge of dynamics of
a system or process (e.g., model of a system or process) to create
optimization visualizations. Advisory component 2202 can tie in financial
information, production schedules, and the like, to quantify an
enterprise manufacturing intelligence (EMI) system. Moreover, advisory
component 2202 can employ drag-and-drop capability/flexibility to handle
"what if" scenarios. In this manner, advisory component 2202 can be
utilized by plant facility management to optimize production processes,
and through facilities provided by visualization component 2212
(discussed infra) such information or input from advisory component 2202
can be used to provide visualizations of production processes. It should
be noted, that advisory component 2202 can dynamically create an
information model, and/or concurrently create a corresponding
visualization.

[0225]Modeling component 2204 can also be included in enterprise resource
planning (ERP) component 184. Modeling component 2204 can be utilized to
build models or sub-models of demand and/or supply, for example, and
associated sources or sinks of such demand and/or supply. Creation of
such demand and/or supply models or sub-models can include utilization of
cost and efficiency attributes, and the like, and can also include
integrating demand models with supply models. Moreover, demand and/or
supply models or sub-models can also be based on historical customer
orders, order size, order accuracy (e.g., to minimize production
overruns), order changes, etc. The developed or created models or
sub-models can be employed to set inventory targets that can in turn
drive or leverage capacity to meet demand which in turn can drive
inventory management, ordering of factors of production, working capital
optimization, and the like. Additionally, modeling component 2204 can
also construct and utilizing stochastic models that can assess the
probability of achieving a stated economic return and/or one or more
optimal operating strategy that satisfy all or some of the input
constraints employed to develop the model.

[0226]As illustrated, enterprise resource planning (ERP) component 184 can
also include facility management component 2206 which can be utilized to
identify areas in a production process where inefficiencies are extant
and methodologies and/or actions that can be utilized to resolve such
inefficiencies. In order to facilitate its goals, facility management
component 2206 can employ the predictive capabilities of prognostics
engine 110 and/or optimization engine 2210 to tune the production process
to lower costs and to increase profitability. The predictive values
generated can optionally include associated probabilistic values such as
for example, the likelihood of achieving the value and the probability of
staying at the predicted value for a specified time period.

[0227]Moreover, enterprise resource planning (ERP) component 184 can
include hierarchical component 2208 that can use multi-variant modeling
and data mining to create hierarchical structures of a model of the
production process. The hierarchical structures generated by hierarchical
component 2208 can include or associate an organizational layer on top of
the multi-variant model. For instance, multiple lines in a production
facility can benefit from advanced process control (APC) from a model on
one particular kiln or the like, and the model can be ported as a type or
class and can thereafter be ported to numerous and disparate lines of
production. It should be noted in this context, that Bayesian types of
models can be adapted based at least in part on specific use rather than
building models from scratch each time, and that utilization of such a
unified model allows for plant or production process design in a manner
analogous to the object oriented programming paradigm. Moreover, it
should be further noted that hierarchical component 2208 can also create
business system types of models. It should also be noted that the models
can also include a suite of coupled sub-models that can be based on
analytic approximations of the production sub-processes. Alternatively or
in addition to the analytic models, production processes can be modeled
as causal models and key performance values extracted from the causal or
hybrid production models. The production processes can also be described
by other model-free estimators such as artificial neural networks or a
combination of model-based and model-free estimators.

[0228]In the context of modeling component 2204, facility management
component 2206, and/or hierarchical component 2208, the value of
predictive enterprise manufacturing intelligence (EMI) is typically a
function of the model abstraction, and/or the plug-and-play nature of the
models. Accordingly, utilization of the claimed subject matter can
provide very sophisticated and unique "what if" situations that can be
used to "sandbox" or prototype various production scenarios in order to
maximize profits and minimize waste. A wide range of "what if" scenarios
can be generated and evaluated according to a cost function or economic
valuation method. Other generative and search methods such as genetic
algorithms may be used to search the space of feasible scenarios to
identify an optimal production scenario.

[0229]In accordance with an aspect of the claimed subject matter modeling
component 2204, facility management component 2206, and/or hierarchical
component 2208 can develop and employ principal component type models
(e.g., models running without any inputs--the model runs and evolves over
time). Such principle component type models can provide estimations of
attributes that typically cannot be measured with ease and further can
provide an understanding of how situations can evolve. Scenario
generation and evolution can be described using a state transition model.
Values can be assigned to each state corresponding to the expected return
from operating in that particular production condition. State transition
links can indicate the cost, risk, and probability of transitioning to a
neighboring more desirable or less desirable state.

[0230]Further, in accordance with further aspects of the claimed subject
matter modeling component 2204, facility management component 2206,
and/or hierarchical component 2208 can in conjunction or separately
utilize global type models. Global type models can be perceived as a type
of dynamic modeling for use with the unified production model wherein
various attributes of the model can be adjusted dynamically or in
real-time. Moreover, in accordance with a further aspect of the claimed
subject matter, modeling component 2204, facility management component
2206, and/or hierarchical component 2208 can utilize existing or
dynamically created models to dynamically and/or automatically (e.g.,
recursively and/or iteratively) generate sub-models based at least in
part on physical changes to a production process and/or production
facility.

[0231]The claimed matter therefore can provide a scalable platform that
provides for advanced process control, optimization, and/or closed-loop
control systems. The matter as claimed therefore can verify and validate
existing or dynamically created models that can be implemented online and
which can permit a local facility control engineer to interact with the
models. Additionally, by incorporating advanced process control (APC)
aspects and utilizing financial information with the dynamically created
models, the models so generated can allow for cross-platform sharing of
sub-models so that various vertical domains can share models (e.g.,
through utilization of cut and paste modalities) without the necessity of
domain expertise in the various areas of production or with the
associated models. Furthermore, the disclosed subject matter can build in
constraints that prevent invalid models from being built or created. By
building rich intelligence into developed or created models, when these
models are deployed they can automatically, dynamically, and continuously
learn the production process being modeled and in so doing identify
interdependencies or correlations to use in connection with future
constraints that might arise in a production process. In addition, the
claimed and disclosed matter can facilitate or actuate an inventory
management aspect wherein production schedules can be employed to
determine when and/or whether to order new inventory, or inventory of
better or lesser quality. For example, if a production process utilizes a
factor of production with ash content, it might be determined through
utilization of the claimed matter that the ash content of the input is
sub-optimal in which case input with a higher or lower ash content might
need to be ordered so that the production process can be rendered
optimal. The dynamically created models may run in parallel with the
actual production process. Deviations observed between the model and the
actual production process can form variances or residuals. The residuals
can be analyzed and used to identify problems or faults in the equipment
or the process and permit efficient problem detection and diagnosis. The
analysis of residuals can also indicate faulty assumptions or gaps in the
model. If faults are detected, the dynamic model can be used to define
and validate an alternative compensating production process that will
mitigate the effect of the failed component or process until corrective
action can be taken. Given that suitable reliability and production
levels are met, the new production process can then be implemented as an
interim solution. Yet another role for the dynamic process model is to
provide a basis for defining a new production facility or production
process, The model can be used to define a new, superior model that
provide improved economic return, less variability, and more robust
production operation. Various potential production processes can be
generated and evaluated without the constraints imposed due to existing,
perhaps outdated, equipment, procedures, materials, and processes.

[0232]In a further aspect, enterprise resource planning (ERP) component
184 can include optimization engine 2210 that can be applied beyond
processes or control of processes to scheduling and/or economic
optimization of processes or production facilities management wherein
such scheduling and/or economic optimization can be carried out in
real-time. For instance, plant or process scheduling can be carried out
in real-time and can be based on current data. As will be appreciated the
developed model (e.g., provided by modeling component 2204) can be
tightly coupled to live data and as such can be utilized to predict
forward as part of the optimization process, marrying closed loop control
to key performance indicators.

[0233]In facilitating its aims, optimization engine 2210, as well as any
other component or aspect associated with enterprise resource planning
(ERP) component 184, can utilize genetic algorithms as part of the
optimization process or in building models of production processes
wherein inputs and/or outputs can be selected as part of building a
process type. Further, optimization engine 2210 can determine (e.g.,
learn) through data analysis what is to be considered as a normal mode of
operation. In establishing a norm, optimization component 2210 can
utilize a recorded expected behavior and compare it with actual behavior
to ascertain what should be considered normal. In such a manner
optimization engine 2210 can dynamically and adaptively adjust
performance indicators (e.g., key performance indicators (KPIs)) to
reflect the reality of a particular production process rather than vague
theoretical goals. Additionally, optimization engine 2210 also has the
ability to re-use key performance indicators (KPIs) and to obtain
information from persisted sources (e.g., persisted or associated with
store 2216) as well as acquire data from known data sources which can be
leveraged in connection with leveraging non-linear prediction models. The
models generated can also have a stochastic measure assigned that can
indicate the likelihood or certainty of the model and the probability of
achieving the expected production level or economic value.

[0234]Moreover, optimization engine 2210 can also be utilized to optimize
the loading and unloading of resources. For example, optimization engine
2210 in concert with radio frequency identification (RFID) tags can be
utilized to determine how best to load or unload a container with product
or raw materials. Similarly, optimization engine 2210 can further be
utilized to best utilize empty space (e.g., shop floor space, office
space, placement of raw material bins, hazardous material handling, . . .
). These facilities of optimization engine 2210 can be effectuated
through use of linear-regression modalities and/or techniques (e.g.,
traveling salesmen type algorithms).

[0235]Further as illustrated in FIG. 22, enterprise resource planning
(ERP) component 184 can include visualization component 2212 that
provides visualizations of its results (e.g., by way of automatically
and/or dynamically in real-time updateable virtual instrumentation
projection that allows user interaction). Visualization component 2212
can present information in a new way, providing users the ability to look
into the prognosticative future and/or to proactively adjust context. For
example, a production facility engineer can reconfigure a production
facility (e.g., plant or factory floor) to ensure that end-product output
is maximized from every aspect of production. By employing the claimed
matter, and in particular, aspects of visualization component 2212,
multiple dimensions involved in the production of a final product can be
analyzed and negative factors mitigated and positive factors enhanced in
order to ensure maximum efficiency and maximum profitability thereby
minimizing inefficiencies and loss. For instance, where an alarm should
have occurred but never occurred, visualization component 2212, through
the facilities of other components and aspects included in enterprise
resource planning (ERP) component 184, can provide an adaptive
visualization of where the failure occurred. It should be noted in this
context that the claimed matter automatically infers an event (e.g.,
alarm conditions, etc.) based at least in part on real-time input or
incoming historical data rather than on human input. Moreover,
visualization component 2212 can facilitate or effectuate alarm
classifications thereby minimizing the occurrence of cascading alarms and
in so doing facilitating a root cause analysis to identify the root cause
of the alarm condition. For example, in order to identify the root cause
of cascading alarms the modeling structure can be beneficial as a
"hierarchical alarm tree" can be developed as a consequence of
utilization of modeling component 2204 and can be utilized to prune the
"hierarchical alarm tree" to ascertain the root cause of the cascading
alarms. The modeling structure can include a causal modeling component
and a stochastic modeling component and a state transition component.

[0236]Visualization component 2212 further allows production facility
engineers or production facility managers the ability, through user
adaptable dynamic real-time visualizations, to predictively identify
and/or isolate and resolve problem areas before these problem areas
manifest themselves in an actual production run. For instance,
visualization component 2212 can be utilized to predictively visualize
and resolve a production event (or non-event) that will occur in the
future (e.g., 2, 12, 24, 36, 48, 128, . . . , hours into a production
run). Accordingly, for example, real-time control data (e.g., from one or
more industrial controller) can be utilized to automatically populate a
predictive information model that can be developed by the claimed matter.
The predictive information model so constructed can then be utilized to
provide rich visualizations that allows for gleaning information
regarding a process or production system across temporal boundaries as
well as potential optimization goals. Moreover, the claimed matter can
mesh real-time data with hypothetical data in order to provide
dynamically adaptive, predictive models. The predicted state or states
can have associated with them the probability the future condition or
production event will occur and the probability it will occur at a
particular time in the future. This can permit taking action such as
altering the control, production rate, or equipment configuration to
avoid a problematic state or undesirable production event. Visualization
component 2212 can include a facility for identifying unusual or
"interesting" conditions or events and highlighting these in the
presentation to the operator. The criteria for classifying a condition as
unusual or "interesting" can be made based at least in part on the expect
value or the value of the model-predicted condition. In addition,
persistent data and real-time data can be routinely screened using
established data mining techniques. Unusual conditions or trends can be
identified and presented using visualization component 2212. Data mining
techniques such as statistical measures (e.g., principal component
analysis), artificial neural networks (e.g., unsupervised Kohonen maps),
and search agents (e.g., autonomous agents) can be employed to
continually inspect the growing based of production and economic data.

[0237]Additionally, enterprise resource planning (ERP) component 184 can
include training component 2214 that can utilize previously constructed
models to dynamically simulate various outcomes in order to provide a
training sandbox wherein apprentice users and/or seasoned professional
production facility managers can test various plant and production
configurations in order to learn the best ways of optimizing and/or
maximizing a production process. Alternatively, training component 2214
can be used to inject serious fault and anomalous conditions to determine
the response of the system, the operator response, and the reaction of
the system to the operator's response. A sequence of stimulus-response
events can be generated and evaluated. Training component 2214 can
include an evaluation module that can establish the skill level of the
person being trained and identifies areas of strength and weakness.
Subsequent training and automatically generated scenarios can be directed
at improving the weak areas identified. The training module can
optionally include an expert operator module and an expert teacher
module. The expert operator module represents the response an expert
operator would have for different operating conditions. The expert
teacher module assesses the students competencies and provides cues as
needed, permits exploratory search and investigation by the student, and
at the appropriate time, give the student the correct answer along with
an explanation. During training, the trainee's response may be compared
to the expert operator modules and the expert teacher module will
establish a student model that will guide the teacher module in
determining the students competency and establishing a teaching strategy
(e.g., immediately correct the student, permit the student to explore the
implications of an incorrect decision, provide hints or cues to the
student, . . . ) and in carrying out the strategy and evaluating the
students progression in learning. The training module can also include
integrating real-time data to permit the student to see the result of
various decisions on an actual production process.

[0238]Store 2216 can also be included with enterprise resource planning
component 184. Store 2216 provides the ability to persist trajectories
into a historian aspect of the claimed subject matter. The historian
aspect of the disclosed and claimed subject matter permits users (e.g.,
plant facility managers, plant maintenance engineers, etc.) to inform the
predictive and optimization aspects of the claimed matter (e.g.,
optimizer engine 2210 and/or prognostics engine 110) with putative
conditions that the user deems necessary to a more efficient and/or
streamlined operation, the optimization and/or predictive aspects can
thereafter provide models with which the user can interact and
interrogate and visualize (e.g., through visualization component 2212)
the production process. As depicted store 2216 can include volatile
memory or non-volatile memory, or can include both volatile and
non-volatile memory. By way of illustration, and not limitation,
non-volatile memory can include read-only memory (ROM), programmable read
only memory (PROM), electrically programmable read only memory (EPROM),
electrically erasable programmable read only memory (EEPROM), or flash
memory. Volatile memory can include random access memory (RAM), which can
act as external cache memory. By way of illustration rather than
limitation, RAM is available in many forms such as static RAM (SRAM),
dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR
SDRAM), enhanced SDRAM (ESDRAM), Synchlink® DRAM (SLDRAM),
Rambus® direct RAM (RDRAM), direct Rambus® dynamic RAM (DRDRAM)
and Rambus® dynamic RAM (RDRAM). Store 2216 of the subject systems
and methods is intended to comprise, without being limited to, these and
any other suitable types of memory. In addition, it is to be appreciated
that store 2216 can be a server, a database, a hard drive, and the like.
Store 2216 can include data in compressed or encoded form and can exist
in multiple distributed data stores. Data stores can reside in a computer
room, server room, computer-based production machine, programmable logic
controller (e.g. PLC), intelligent device, or a smart sensor node and any
combination of the above. Data can be accessed virtually as if it was a
central database residing in one location.

[0239]Additionally, enterprise resource planning component 184 can further
include scenario generator 2218 that can automatically and/or dynamically
generate and search through a wide range of plausible scenarios and can
select one or more optimal operating strategies that can satisfy some or
all the input constraints. In facilitating its aims scenario generator
2218 can utilize stochastic models that can assess the probability of
achieving stated goals, as well as can consider temporal aspects of
plausible scenarios. For example, a high-return scenario that lasts for a
very short duration can be inferior to a longer term, more stable
scenario that generates a slightly less economic return.

[0240]FIG. 23 provides depiction of an illustrative method 2300 that can
be utilized to provide an energy optimization model in accordance with an
aspect of the claimed subject matter. Method 2300 can commence at 2303
where variable costs associated with an entity's or organization's
business system can be utilized to construct economic sub-models for each
energy-generating asset at a production facility. The sub-models so built
can be employed to determine each asset's financial profile, taking into
consideration their respective generating capacity, efficiency curves,
and operating costs. Other factors such as reliability, maintenance cost,
and life-cycle costs can also be included. Each of these asset sub-models
are then combined to create the production facility's energy-supply
model. At 2304 the optimization component and/or prognostics engine of
the claimed subject matter can be utilized to create a sub-model of
production to determine, at a user-defined time horizon, the predicted
energy demand based at least in part on current and/or future operating
objective. This sub-model can be considered the production facility's
energy-demand model. At 2306 the energy demand and supply models can be
integrated utilizing the modeling framework of the claimed subject matter
to solve for the economic supply optimum and expose the most
cost-effective energy-generating asset available to meet predicted
demand. This integrated demand and supply model becomes the energy
optimization model. The energy demand and supply models can be integrated
in series, parallel, nested, or in a networked structure to achieve the
most efficient solution for an economic problem. The goal of method 2300
is to provide timely visibility into the most cost-effective source of
energy to meet the predicted demand from production, while ensuring full
environmental compliance. Other factors such as probability the predicted
energy demand profile will exist and the expected variability in this
demand, supply equipment reliability, and certainty of providing the
target energy levels in the future, the estimated cost in the future to
provide the target energy level, the predicted cost of energy, and the
estimated cost of energy produced can also be included in the model.

[0241]FIG. 24 exemplifies an illustrative method 2400 that can be utilized
to provide dynamic capacity management in accordance with an aspect of
the claimed subject matter. Method 2400 can commence at 2402 where
ascertainment can be made as to the current production rate. At 2404 a
prediction (e.g., utilizing the various components associated with
enterprise resource planning (ERP) component) can be made. The prediction
is the theoretical capacity of a facility's production. At 2406 a
visualization can be generated or more specifically projected or rendered
onto a display (e.g., computer monitor, and the like). This visualization
can then be employed to drive towards the determined theoretical capacity
as well as to identify bottlenecks to achieving the theoretic goal.
Moreover, the visualization can also be utilized to identify to
management historical bottlenecks and facilitate the mitigation of such
bottlenecks. As will be appreciated by those of reasonable skill in the
art, the visualization can also provide executives or production facility
engineers the ability to redesign a system or process in order to
optimize the process as well as to make smart financial decisions. The
prediction of theoretical capacity in 2404 can also include a cost
function that assigns a cost to produce for the various possible
production levels. The cost function can include energy, support
services, maintenance and reliability costs and other life cycle cost
factors. This cost reflects the potential loss of efficiency and
increased failure rate when running equipment at or near the theoretical
limit. It may indicate that it is not economically prudent to run
equipment at the theoretical maximum capacity. An economic optimization
model can be used to establish an economically viable maximum capacity
that may be less the physical theoretical capacity.

[0242]FIGS. 25-31 provide depiction of various illustrative visual
instrumentations that can be generated and displayed or rendered on a
display device, for example. As will be appreciated by those of ordinary
skill, one or all the various and disparate visual instrumentations can
be simultaneously generated and/or displayed or rendered on a particular
display device. Moreover, it should also be noted that the generated
and/or displayed or rendered illustrative visual instrumentation can be
subject to direct user interaction (e.g., using tactile manipulation).
The displays can include a combination of persistent data, real-time
data, computed data, model-generated data, and user-entered data. User
input permits exploratory searches and user-driven data analysis and
scenario planning. As illustrated FIG. 25 provides a visual
instrumentation 2500 that depicts grade profitability over a time horizon
(e.g., the x-axis) measured in uros/ton. Further FIG. 26 provides a
further visual instrumentation 2600 that depicts potential opportunity
over a time horizon measured in uros/ton. FIG. 27 provides visual
instrumentation 2700 of the actual production costs of various factors of
production (e.g., fiber, chemicals, steam, refining, blade, filler, . . .
) measured over a time horizon. FIG. 28 provides depiction of a visual
instrumentation 2800 that illustrates various factors of production, the
sell price and a comparison between the current grade and a theoretical
target. FIG. 29 exemplifies a further visual instrumentation 2900 that
illustrates a theoretical vs. actual ash content (a factor of
production). Visual instrumentation 2900 provides comparison between the
actual content of ash vs. the potential content of ash over a time
horizon. FIG. 30 provides another visual instrumentation 3000 that maps
lost opportunity costs over time and measure in uros. FIG. 31 illustrates
a further visual implementation that depicts controller uptime and ash
content in the current grade. FIG. 31 provides the actual or current
quantity, a target goal and categorizations of poor, fair, and good.

[0243]Although the invention has been shown and described with respect to
certain illustrated aspects, it will be appreciated that equivalent
alterations and modifications will occur to others skilled in the art
upon the reading and understanding of this specification and the annexed
drawings. In particular regard to the various functions performed by the
above described components (assemblies, devices, circuits, systems,
etc.), the terms (including a reference to a "means") used to describe
such components are intended to correspond, unless otherwise indicated,
to any component which performs the specified function of the described
component (e.g., functionally equivalent), even though not structurally
equivalent to the disclosed structure, which performs the function in the
herein illustrated exemplary aspects of the invention. In this regard, it
will also be recognized that the invention includes a system as well as a
computer-readable medium having computer-executable instructions for
performing the acts or events of the various methods of the invention.

[0244]In addition, while a particular feature of the invention may have
been disclosed with respect to only one of several implementations, such
feature may be combined with one or more other features of the other
implementations as may be desired and advantageous for any given or
particular application. As used in this application, the term "component"
is intended to refer to a computer-related entity, either hardware, a
combination of hardware and software, software, or software in execution.
For example, a component may be, but is not limited to, a process running
on a processor, a processor, an object, an executable, a thread of
execution, a program, and a computer. Furthermore, to the extent that the
terms "includes", "including", "has", "having", and variants thereof are
used in either the detailed description or the claims, these terms are
intended to be inclusive in a manner similar to the term "comprising."